Question:
According to the paper, without using SSB, the proportion of effective training samples (with non-zero advantages) decreases from approximately 60% in early stages to below 40% in later stages; using SSB can maintain this proportion above 60%. Assume a later-stage training batch containing 1000000 samples. Based on these boundary values, which of the following inferences hold?
A. Compared to not using SSB, using SSB increases the number of effective samples by at least 202000.
B. The number of "ineffective samples" caused by not using SSB is at least 1.5 times the number of "ineffective samples" when using SSB.
C. Even under the most ideal scenario (i.e., 39.9%), the number of effective samples without SSB is less than 400000.
D. Assuming each effective sample contributes the same gradient value, the total gradient gain from using SSB is at least 50%.

Answer: BC

arXiv:2504.16656v4  [cs.CV]  6 Jun 2025
Skywork R1V2: Multimodal Hybrid Reinforcement
Learning for Reasoning
Chris∗,
Yichen Wei∗,
Yi Peng,
Xiaokun Wang,
Weijie Qiu,
Wei Shen,
Tianyidan Xie,
Jiangbo Pei,
Jianhao Zhang,
Yunzhuo Hao,
Xuchen Song†,
Yang Liu†,
Yahui Zhou
Skywork AI, Kunlun Inc.
chris@kunlun-inc.com, xuchen.song@kunlun-inc.com
Abstract
We present Skywork R1V2, a next-generation multimodal reasoning model and
a major leap forward from its predecessor, Skywork R1V. At its core, R1V2
introduces a hybrid reinforcement learning paradigm that jointly leverages the
Mixed Preference Optimization (MPO) and the Group Relative Policy Optimiza-
tion (GRPO), which harmonizes reward-model guidance with rule-based strategies,
thereby addressing the long-standing challenge of balancing sophisticated reason-
ing capabilities with broad generalization. To further enhance training efficiency,
we propose the Selective Sample Buffer (SSB) mechanism, which effectively
addresses the vanishing advantages dilemma inherent in GRPO by prioritizing
high-value samples throughout the optimization process. Notably, we observe that
excessive reinforcement signals can induce visual hallucinations–a phenomenon we
systematically monitor and mitigate through calibrated reward thresholds through-
out the training process. Empirical results affirm the exceptional capability of
R1V2, with benchmark-leading performances such as 62.6 on OlympiadBench,
78.9 on AIME2024, 63.6 on LiveCodeBench, and 73.6 on MMMU. These results
underscore R1V2’s superiority over existing open-source models and demonstrate
significant progress in closing the performance gap with premier proprietary sys-
tems, including Gemini 2.5 and OpenAI-o4-mini. The Skywork R1V2 model
weights have been publicly released to promote openness and reproducibility‡.
1
Introduction
Recent advancements in “slow-thinking” multimodal models—such as OpenAI-o1 [24], Gemini-
Thinking [32], Kimi-1.5 [34], and Skywork-R1V [26]—have led to substantial progress in complex
reasoning tasks across mathematics and science. These models emulate reflective cognitive processes,
achieving stronger performance than their “fast-thinking” counterparts (e.g., GPT-4o [24] and Claude-
3.5 [2]), which prioritize rapid generation over deliberate reasoning. For instance, slow-thinking
models demonstrate over 30% improvement on benchmarks like AIME24 and AMC23, along with
approximately 10% gains on science-related benchmarks such as GPQA [24, 14, 25].
However, extending slow-thinking strategies to multimodal domains introduces new challenges.
While improvements are observed on visual reasoning tasks such as MMMU [44] and MathVision
[38], performance degrades on general perception benchmarks (e.g., AI2D [18]). This decline is
∗Equal contribution
†Corresponding author
‡https://huggingface.co/Skywork/Skywork-R1V2-38B
Tech report. Preprint.
often accompanied by a rise in visual hallucinations, highlighting a critical issue: How can we
effectively promote slow-thinking behavior in vision-language models (VLMs) without compromising
their generalization capabilities?
To tackle this problem, we introduce Skywork R1V2, a next-generation vision-language model that
acquires multimodal reasoning skills directly via reinforcement learning (RL), bypassing the need for
teacher model distillation. R1V2 is trained on a comprehensive dataset spanning visual perception,
scientific inquiry, and abstract reasoning, encompassing both general-purpose and reasoning-intensive
tasks. The training process utilizes a hybrid reinforcement learning paradigm that jointly leverages
the Mixed Preference Optimization (MPO) [39] and the Group Relative Policy Optimization (GRPO)
[28].
R1V2 first implements MPO with three core learning objectives: (1) the relative preferences between
response pairs, (2) the absolute quality of individual responses, and (3) the process for generating
preferred responses [39]. The preference signals is provided by our Skywork-VL Reward [40]—a
reward model capable of evaluating both multimodal understanding and reasoning tasks. Given these
high-quality preference signals, MPO effectively mitigates hallucinations and overthinking in the
generated outputs, leading to improved performance on general vision tasks.
To further boost the reasoning capabilities, we incorporate the GRPO algorithm, which computes
relative advantages by comparing candidate responses within the same query group. However, as
training progresses, candidate responses tend to converge (i.e., become uniformly correct or incorrect),
resulting in vanishing advantage signals and limited reasoning diversity. Building upon insights
from recent work on this challenge [37], we employ the Selective Sample Buffer (SSB), which
caches high-quality training examples with non-zero advantages and reintroduces them during policy
updates. This mechanism increases gradient density, enhances training stability, and encourages
deeper reasoning paths. The hybrid of MPO and GRPO enables a more effective balance between
reasoning specialization and generalization.
Extensive evaluations demonstrate the effectiveness of R1V2 across multiple authoritative multi-
modal benchmarks, including 62.6% on OlympiadBench [13], 78.9% on AIME2024, 63.6% on
LiveCodeBench [15], and 73.6% on MMMU. These results not only establish new open-source
baselines but also substantially reduce the performance gap with proprietary state-of-the-art models
such as Gemini 2.5 [8] and OpenAI-o4-mini [25].
In summary, Skywork R1V2 offers a promising and scalable framework for training robust and
reflective multimodal reasoning agents via direct reinforcement learning. It highlights the potential of
unifying high-level reasoning and perceptual understanding, paving the way for the next generation
of general-purpose AI systems.
2
Related Work
2.1
Multimodal Reasoning Models
Recent advances in multimodal AI have increasingly focused on enhancing reasoning capabilities
across different modalities. Proprietary models like Claude-3 [2], GPT-4o [24], and Gemini [33]
have demonstrated remarkable capabilities, while the open-source community has responded with
competitive alternatives such as LLaVA [21], Qwen-VL [3], and InternVL [6].
Recent innovations have shifted toward slow-thinking approaches (e.g., OpenAI-o1 [24], Gemini-
Thinking [32], and Kimi-1.5 [34]), which introduce specialized mechanisms for extended deliberation.
These models allocate additional computational resources to complex reasoning tasks, demonstrat-
ing substantial improvements on mathematical and scientific benchmarks. The first generation of
Skywork-R1V [26] pioneered the direct application of text reasoning capabilities to vision through
advanced adapter techniques, establishing a new paradigm for multimodal reasoning. Concurrently,
VL-Rethinker [37] enhanced multimodal slow-thinking through reinforcement learning with Selective
Sample Replay, achieving strong performance on mathematical reasoning benchmarks.
However, a common challenge across these approaches is maintaining balance between specialized
reasoning abilities and general-purpose multimodal understanding. Models optimized heavily for
mathematical reasoning often demonstrate degraded performance on everyday visual tasks, while
2
提升模型在特定领域慢思考能力的同时, 又保证了他的通用能力不下降.
general-purpose models struggle with complex analytical reasoning. Hence, addressing the trade-off
between reasoning specialization and generalization is one of the key motivations behind our work.
2.2
Preference Optimization in Multimodal Models
Preference optimization has emerged as a powerful paradigm for aligning AI systems with human
expectations, though its extension from language-only to multimodal reasoning presents significant
challenges. While foundational techniques like RLHF [7] and DPO [27] have transformed text-based
models, their direct application to multimodal contexts requires substantial adaptations to accommo-
date the complexity of cross-modal interactions. Recent works on multimodal preference learning
[31, 39] have incorporated human preferences into vision-language alignment, enhancing response
quality in general multimodal tasks. Despite these advances in addressing straightforward visual
question-answering scenarios, the application of preference optimization to complex multimodal
reasoning remains relatively underexplored, with two critical limitations hindering progress in this
domain. First, the binary nature of typical preference pairs fails to capture the nuanced progression of
complex reasoning paths, where multiple equally valid solutions may exist with different intermediate
steps. Second, existing reward models predominantly evaluate textual quality in isolation, overlooking
the crucial relationship between visual interpretation and logical inference that defines successful
multimodal reasoning.
3
Methodology
This section introduces the core methodology behind Skywork R1V2. Building upon the original
R1V series, R1V2 incorporates a hybrid training strategy combining reinforcement learning and
reward-model-guided preference learning to better balance reasoning capability and general-purpose
performance.
3.1
Efficient Multimodal Transfer via Modular Reassembly
To reduce reliance on large-scale multimodal reasoning data, we decouple the alignment of visual-
language representations from the preservation of reasoning capabilities. Specifically, we introduce a
lightweight multi-layer perceptron (MLP) adapter, denote as fc, to bridge a frozen vision encoder fv
with a reasoning-capable language model fl. Here we choose InternViT-6B [6] to be vision encoder
and QwQ-32B [36] to be our language model. Formally, given a visual input xv and a text input xt,
the overall process represented as:
y = fl(fc(fv(xv)), xt)
(1)
where fv extracts visual features, fc adapts these features for capability with the language model,
and fl integrates both the adapted visual features and the textual input xt to perform reasoning and
generate the output y.
Unlike the first generation of R1V, R1V2 eliminates the supervised fine-tuning (SFT) stage. Recent
finding suggests that SFT can inadvertently undermine the performance of subsequent reinforcement
learning or reasoning processes [5], which can hinder the model’s ability to develop genuine reasoning
behaviors. Rather than relying on SFT, R1V2 adopts a modular approach that directly connects a
pretrained reasoning language model with a visual adapter. While this approach leads to a slight
reduction in general visual understanding, it preserves the inherent reasoning ability of the language
model and significantly benefits overall reasoning performance by avoiding the degradation introduced
by SFT.
We systematically experimented with freezing and activating different model components, and
observed a remarkable phenomenon: capabilities in text and vision exhibit high transferabil-
ity—improvements in one modality directly benefit the other. Notably, while training the vision
encoder alone yields limited gains, both adapter-only training and joint LLM+adapter training prove
highly effective, suggesting that cross-modal alignment rather than visual encoding represents the
critical bottleneck in multimodal reasoning.
3
3.2
Mixed Preference Optimization
To achieve a strong model before reinforcement learning and balance reasoning with generalization,
we employ Mixed Preference Optimization (MPO) [39], which demonstrates successful in the
Internvl series model, as a crucial component of our optimization pipeline. The alignment of our
R1V2 model is significantly enhanced by our Skywork-VL reward model [40], which guides our
iterative optimization process using MPO. Noticeably, the process yields a substantial reduction in
the occurrence of repetitive chain-of-thought (CoT) and overthinking in the generated output.
MPO loss function can be typically expressed as follows:
L = w1Lpreference + w2Lquality + w3Lgeneration.
(2)
Lpreference is typically a DPO [46] loss which can optimize the relative preference between positive
and negative samples:
Lpreference = −log σ

β log πθ (yc | x)
π0 (yc | x) −β log πθ (yr | x)
π0 (yr | x)

(3)
Where β is the KL penalty coefficient, and the prompt, positive sample, and negative response are
represented by x, yc, and yr, respectively. The policy model πθ is initialized from π0.
Lquality is a BCO [17] loss. This loss helps the model to understand the absolute quality of individual
responses. This algorithm trains a binary classifier, where the logit serves as a reward and effectively
maps the chosen response to 1 and the rejected response to 0. The loss function is defined as:
Lquality = L+
quality + L−
quality
= −
"
log σ

β log πθ (yc | x)
π0 (yc | x) −δ

+ log σ

−

β log πθ (yr | x)
π0 (yr | x) −δ
 #
(4)
Here, δ is practically computed as the moving average of past rewards to stabilize training, which is a
typically method in online RL method [1, 29].
Furthermore, the generation loss:
Lgeneration = −log πθ(yc|x)
|yc|
(5)
which is typically a Negative Log Likelihood loss (NLL), guiding the model to learn the chosen
response, aiming to reduce the distribution shift between the base model’s outputs and the preferred
responses.
Mixed Preference Optimization strategy can integrate preference signals from the Skywork-VL reward
model [40] with hand-crafted rule-based constraints (e.g., format correctness, factual consistency,
step-by-step reasoning completeness). This hybrid reward structure better aligns the model’s outputs
with both stylistic preferences and factual requirements across modalities.
3.3
Reinforcement Fine-tuning
In the reinforcement fine-tuning stage for VLMs, we primarily employ the GRPO Algorithm with a
hybrid supervised signal composed of both rule-based reward and model-based reward. Additionally,
we leverage the SSB mechanism to further enhance the efficiency of our reinforcement learning
process.
3.3.1
GRPO Algorithm with Hybrid Reward Signal
For enhanced reasoning in multi-modal contexts, we adapt Group Relative Policy Optimization
(GRPO) [28], a common RL algorithm originally developed for text-only LLMs. GRPO is a policy
optimization algorithm designed to compute token-level advantage estimates by performing intra-
group comparisons of generated responses conditioned on a specific query. For a given input instance
x, a behavior policy πθold samples a batch of N candidate responses {yi}N
i=1. The advantage ˆAi,t for
the i-th response at time step t is determined by normalizing the rewards obtained across the response
group, according to:
ˆAi,t = r(x, yi) −mean({r(x, y1), ..., r(x, yN)})
std({r(x, y1), ..., r(x, yN)})
(6)
4
To mitigate the “alignment tax” [20] in reasoning ability, we again utilize our Skywork-VL reward
[40] model to introduce a preference reward signal rθ, supplementing the rule-based reward rrule.
Furthermore, we incorporate a format reward rformat to align the model’s output with the DeepSeek
R1-style [9] chat template. Thus, our hybrid reward function is defined as follows:
r(x, yi) = rrule(x, yi) + rθ(x, yi) + rformat(x, yi)
(7)
The GRPO optimization objective incorporates a clipped surrogate loss term augmented with a
KL-penalty to ensure stable policy updates, formulated as:
LGRPO(θ) = 1
G
G
X
i=1
1
|yi|
|yi|
X
t=1
min
 πθ(yi,t | xi, yi,<t)
πθold(yi,t | xi, yi,<t)
ˆ
Ai,t, clip
 πθ(yi,t | xi, yi,<t)
πθold(yi,t | xi, yi,<t) , 1 −ϵ, 1 + ϵ

ˆ
Ai,t

(8)
The hyperparameter ϵ dictates the tolerance for policy deviation. The clipping function serves to
prevent excessively large policy updates by constraining the ratio of the current policy to the reference
policy within a predefined interval. This mechanism promotes training stability and mitigates the risk
of performance degradation resulting from overly aggressive updates.
3.3.2
Addressing Vanishing Advantages via SSB
However, the direct application of GRPO to VLMs encounters challenges due to its limited effec-
tiveness, particularly after the utilization of MPO. As noted in recent work [37], we also observed
the detrimental “Vanishing Advantages” phenomenon during GRPO training, which can hinder the
upper bound of the optimization. This issue arises when all responses within a query group converge
towards uniform correctness or incorrectness, causing the relative advantage signals to diminish and
impeding effective gradient-based policy updates.
Figure 1: The Selective Sample Buffer (SSB) mechanism addresses the “Vanishing Advantages”
problem by preserving and prioritizing high-value samples with non-zero advantages.
This approach complements concurrent efforts such as Selective Sample Replay [37], while focusing
specifically on advantage-based sample caching mechanisms. This approach prioritizes samples
based on the absolute value of their advantage signals through weighted sampling, strategically
reintroducing these informative samples during policy updates. This method maintains a gradient-rich
training environment even as model responses converge.
A significant empirical observation is the dramatic reduction in effective training samples (those
exhibiting non-zero advantages) from about 60% at the start to under 40% in later phases. This severe
drop-off cripples training efficiency. The SSB mechanism actively combats this by guaranteeing a
continuous source of valuable training signals. Moreover, we discovered that incorporating SSB into
the rollout of offline inference time in advance, effectively creating a filtered prompt pool, yields a
substantial improvement of over 10% in training efficiency during the initial optimization.
Our experiments demonstrate that the SSB mechanism substantially increases training efficiency, with
performance improvements equivalent to full training achieved using just a fraction of the samples.
This approach not only addresses the “Vanishing Advantages” problem but also prevents the model
from plateauing in mid-training, ensuring continuous improvement in reasoning capabilities.
Additionally, we noted that while visual reasoning and textual reasoning capabilities show com-
plementary patterns during training, excessive emphasis on visual reasoning can lead to increased
5
hallucination—a phenomenon we believe occurs because stronger visual reasoning necessitates more
creative interpolation between visual elements. The SSB helps maintain an appropriate balance
between these modalities by preserving diverse learning signals across both domains.
4
Experiments
We conducted comprehensive evaluations of Skywork R1V2 across multiple benchmarks designed to
assess both text reasoning and visual reasoning capabilities.
4.1
Experimental Setup
Benchmarks
We evaluated R1V2 on the following benchmarks:
4.1.1
Text Reasoning Benchmarks
• AIME 2024 [16]: This benchmark includes competition problems from the 2024 American
Invitational Mathematics Examination (AIME), a prestigious and highly selective contest
for elite high school students. It assesses advanced mathematical competencies, requiring
deep conceptual understanding and rigorous logical reasoning skills.
• LiveCodebench [15]: A comprehensive evaluation framework for assessing coding capabil-
ities across multiple programming languages. It features problems ranging from algorithmic
challenges to software design tasks, focusing on the model’s ability to understand, debug,
and generate accurate code solutions under varying complexity constraints.
• LiveBench [41]: A dynamic reasoning benchmark designed to evaluate models’ perfor-
mance on everyday reasoning tasks. It combines diverse problem types including logical
puzzles, causal reasoning, and counterfactual thinking to assess general reasoning capabili-
ties beyond specialized domains.
• IFEVAL [47]: A benchmark designed to evaluate conditional reasoning in large language
models, focusing on the models’ ability to correctly interpret and follow “if-then” statements.
It tests for both factual accuracy and logical consistency in responding to hypothetical
scenarios.
• BFCL [43]: The Benchmark for Faithful Chain-of-thought Language reasoning, which
evaluates models on their ability to generate coherent, logically sound reasoning chains. It
specifically assesses whether models can maintain logical consistency throughout multi-step
reasoning processes without introducing contradictions or non-sequiturs.
4.1.2
Multimodal Reasoning Benchmarks
• MMMU [44]: The Massive Multi-discipline Multimodal Understanding benchmark, which
evaluates models across 30 academic disciplines including STEM, humanities, and social
sciences. It contains college-level problems requiring both visual interpretation and domain-
specific reasoning.
• MathVista [22]: A comprehensive benchmark for evaluating mathematical reasoning
in visual contexts. It contains diverse mathematical problems that require interpretation
of diagrams, charts, geometric figures, and real-world images, testing both perceptual
understanding and mathematical problem-solving.
• OlympiadBench [13]: A challenging benchmark comprising problems adapted from in-
ternational science and mathematics olympiads. It requires advanced reasoning in physics,
chemistry, biology, and mathematics, with problems presented in multimodal formats in-
cluding diagrams, graphs, and equations.
• MathVision [38]: A specialized benchmark focusing on geometric reasoning and spatial un-
derstanding. It contains problems that require analyzing complex geometric configurations,
understanding 3D projections, and applying mathematical principles to visual scenarios.
• MMMU-Pro [45]: An extended version of the MMMU benchmark with more challenging
problems and a focus on professional-level content. It emphasizes interdisciplinary reasoning
and complex visual interpretation tasks that require expertise across multiple domains.
6
Figure 2: Performance comparison on text reasoning benchmarks.
Evaluation Setup
In our evaluations, the maximum generation length is set to 64K tokens for
all benchmarks. We employ a unified evaluation framework using LLM Judge (OpenAI-o4) for
both text reasoning and multimodal reasoning benchmarks. This approach systematically compares
the model’s reasoning responses with reference answers, assessing semantic equivalence and math-
ematical accuracy rather than relying on exact string matching. The LLM Judge can recognize
when expressions differ in form but represent logically consistent solutions, making it particularly
valuable for mathematical reasoning tasks where multiple solution paths or notations may be valid.
For multiple-choice questions, we use a unified prompt that requires the model to provide a boxed
answer format, while for open-ended questions, we instruct the model to present a step-by-step
reasoning process culminating in a clearly boxed final answer. The reported performance metric is
the Pass@1 score, averaged across 5 independent runs to ensure statistical reliability and account for
any variations in model outputs.
Baselines We conduct comprehensive evaluations against several strong proprietary models, including
Claude-3.5-Sonnet (20241022) [2], OpenAI-o4-mini [24], OpenAI-o1 [14], Gemini 2 Flash [32], and
Kimi k1.5 longcot [34]. Additionally, we compare our method with advanced open-source models,
such as Skywork-R1V1 [26], InternVL3-38B [48], QvQ-Preview-72B [35], Deepseek R1 [9] and
Qwen2.5-VL-72B-Instruct [4]. For InternVL3-38B and other models where we report results not
directly from their original papers, we conducted independent evaluations using our unified evaluation
framework to ensure fair comparison.
4.2
Main Results
Text Reasoning Performance
Figure 2 presents the performance of Skywork R1V2 compared to
other state-of-the-art models across various text reasoning benchmarks. Skywork R1V2 demonstrates
exceptional reasoning capabilities, achieving 78.9% on AIME24, 63.6% on LiveCodebench, 73.2%
on LiveBench, 82.9% on IFEVAL, and 66.3% on BFCL.
Notably, R1V2 significantly outperforms its predecessor R1V1, with improvements of 6.9 percentage
points on AIME24, 6.4 points on LiveCodebench, 18.6 points on LiveBench, 10.4 points on IFEVAL,
and 12.8 points on BFCL. This consistent improvement across all benchmarks demonstrates the
effectiveness of our proposed hybrid reinforcement learning approach.
When compared to larger models like Deepseek R1 (671B parameters), our R1V2 (38B parameters)
achieves competitive results, even outperforming it on LiveBench (73.2% vs. 71.6%) and BFCL
(66.3% vs. 60.3%). This suggests that our approach enables efficient learning with fewer parameters,
making R1V2 a more practical choice for deployment scenarios with limited computational resources.
Multimodal Reasoning Performance
Figure 3 shows R1V2’s performance on multimodal reason-
ing benchmarks compared to other open-source models. Our model achieves state-of-the-art results
7
Figure 3: Comparison with open-source models on multimodal reasoning benchmarks.
Figure 4: Comparison with proprietary models on multimodal reasoning benchmarks.
among open-source models of similar parameter scale, with scores of 73.6% on MMMU, 74.0% on
MathVista, 62.6% on OlympiadBench, 49.0% on MathVision, and 52.0% on MMMU-Pro.
More impressively, R1V2 outperforms even larger models such as Qwen2.5-VL-72B (70.2% vs.
73.6% on MMMU) and QvQ-Preview-72B (70.3% vs. 73.6% on MMMU).
Particularly noteworthy is R1V2’s exceptional performance on OlympiadBench, where it achieves
62.6%, substantially outperforming larger models like Qwen2.5-VL-72B (40.4%) and QvQ-Preview-
72B (33.2%). This demonstrates R1V2’s superior capability in complex mathematical reasoning
tasks that require deep analytical thinking and structured problem-solving.
Figure 4 presents a comparison between R1V2 and leading proprietary models. Despite having
significantly fewer parameters, R1V2 demonstrates competitive or superior performance against
several commercial models.
On the MMMU benchmark, R1V2 achieves 73.6%, outperforming Claude 3.5 Sonnet (70.4%),
Gemini 2 Flash (70.7%), and Kimi k1.5 longcot (70.0%). Similarly, on MathVista, R1V2’s score of
74.0% surpasses Claude 3.5 Sonnet (67.7%) and is competitive with Gemini 2 Flash (73.1%) and
Kimi k1.5 longcot (74.9%).
While larger proprietary models like OpenAI-o4-mini still maintain an advantage on these bench-
marks, the gap has significantly narrowed compared to previous open-source efforts. This demon-
strates that R1V2’s innovations in reinforcement learning and multimodal integration are effectively
closing the performance gap between open-source and proprietary systems.
8
Figure 5: Reasoning Capability of Skywork-R1V2 on Chinese Gaokao Physical Problems.
Figure 6: Reasoning Capability of Skywork-R1V2 on Chinese Gaokao Mathematical Problems.
9
Table 1: Comprehensive performance comparison across text and multimodal reasoning benchmarks.
Model
MMMU Math- Math- Olympiad AIME LiveCode
Live
IFEVAL
Vista
Vision
Bench
24
bench
Bench
Proprietary Models
Claude-3.5-Sonnet
70.4
67.7
-
-
-
-
-
-
Gemini-2-Flash
70.7
73.1
41.3
-
-
-
-
-
Kimi-k1.5-longcot
70.0
74.9
53.3
-
-
-
-
-
OpenAI-o1
-
-
-
-
74.3
63.4
72.2
-
OpenAI-o4-mini
81.6
84.3
58.0
-
93.4
74.6
78.1
-
Open-Source Models
Skywork-R1V1
68.0
67.0
-
-
72.0
57.2
54.6
72.5
DeepseekR1-671B
-
-
-
-
79.8
65.9
71.6
83.3
InternVL3-38B
70.1
75.1
34.2
-
-
-
-
-
Qwen2.5-VL-72B
70.2
74.8
38.1
40.4
-
-
-
-
QvQ-Preview-72B
70.3
71.4
35.9
33.2
-
-
-
-
Skywork-R1V2 (Ours)
73.6
74.0
49.0
62.6
78.9
63.6
73.2
82.9
Comprehensive Performance Analysis
Table 1 provides a holistic view of R1V2’s performance
across both text and multimodal reasoning benchmarks compared to leading open-source and propri-
etary models. This comprehensive analysis underscores R1V2’s position as a leading open-source
reasoning model with capabilities approaching or exceeding those of much larger commercial systems.
The results clearly demonstrate that R1V2 achieves state-of-the-art performance among open-source
models across most benchmarks while requiring significantly fewer parameters than competitors.
For example, despite being 2.9x smaller than Qwen2.5-VL-72B and QvQ-Preview-72B, R1V2
outperforms them by 3.4 and 3.3 percentage points respectively on MMMU, and by an even larger
margin on OlympiadBench (62.6% versus 40.4% and 33.2%).
When compared with proprietary models, R1V2 demonstrates competitive performance, particularly
on mathematical and scientific reasoning tasks. Our model’s performance on AIME24 (78.9%) outper-
forms OpenAI-o1 (74.3%) and approaches that of specialized proprietary systems like DeepseekR1-
671B (79.8%), confirming the effectiveness of our hybrid optimization approach in enhancing
complex reasoning capabilities.
This comprehensive evaluation highlights the dual strengths of R1V2: exceptional reasoning capabili-
ties and competitive multimodal understanding, establishing a new baseline for efficient open-source
multimodal reasoning models.
Performance on General Vision Tasks
While R1V2 is optimized for reasoning capabilities, we
also evaluated its performance on general vision understanding tasks to assess potential trade-offs. In
document understanding, R1V2 maintains competitive performance (81.3% on AI2D [18], 79.0% on
ChartQA [23] and TextVQA [30]) despite its reasoning focus, showing only a modest gap compared
to specialized models. For video understanding, R1V2 achieves acceptable results (60.2% on Video-
MME [11], 61.5% on MVBench [19]) but demonstrates stronger capabilities when temporal reasoning
is required (1.92 on MMBench-Video [10] compared to InternVL2.5-38B’s 1.82).
Our analysis of hallucination tendencies revealed an important finding: while aggressive optimization
for reasoning initially increased hallucination rates, our MPO approach effectively mitigated this
issue, reducing hallucination from 18.4% with standard SFT to 8.7% with our full implementation.
This balance allows R1V2 to maintain reasonable factual accuracy (68.9% on RealWorldQA [42])
while excelling at complex reasoning tasks. These results highlight the inherent trade-off between
specialized reasoning and general visual understanding capabilities, with our current design delib-
erately prioritizing reasoning performance. Future versions will focus on enhancing general visual
capabilities while preserving the strong reasoning foundation established in R1V2.
4.3
Qualitative Analysis
Figure 5 demonstrates R1V2’s physics reasoning capabilities through its analysis of an electromag-
netic problem involving rotating magnetic fields and AC generators. When presented with a diagram
10
Table 2: Ablation study on the effect of Selective Sample Buffer (SSB).
Method
Effective Samples
MMMU
MathVista
GRPO w/o SSB
< 40%
73.4
74.0
GRPO w/ SSB
> 60%
73.6
74.0
showing two coils with different numbers of turns positioned perpendicular to each other, the model
methodically evaluates each option by applying fundamental electromagnetic principles. R1V2
correctly identifies that while coil turns differ (n1 > n2), the frequency of the induced alternating
current depends on the rotational speed of the magnetic core rather than the number of turns: “Option
B: The frequency of AC current produced by the two coils is equal; the frequency is determined by
the rotational speed of the magnetic iron...” This analysis demonstrates R1V2’s ability to separate
the relevant physical parameters (rotational frequency) from irrelevant ones (number of turns) when
determining AC frequency, showcasing its understanding of electromagnetic induction principles.
The model’s systematic elimination of incorrect options based on physical laws rather than pattern
matching highlights its robust scientific reasoning capabilities.
Figure 6 showcases Skywork R1V2’s mathematical reasoning capabilities on a complex 3D geometry
problem from the Chinese Gaokao. Faced with determining the volume of a sphere containing a
square-based pyramid, R1V2 develops a structured solution approach: “My thought process might be:
First step: Determine the relationship between the square base and the sphere... Second step: Consider
the height of the pyramid...” The model strategically establishes a coordinate system, positioning
the sphere’s center at the origin and the base in the xy-plane, then accurately applies the volume
formula (4/3)πR3 = (4/3) · π · 8 = 32π/3. Particularly noteworthy is R1V2’s self-verification
behavior, where it explicitly states “Let me check my steps again carefully to see if there are any
errors...” before confirming its answer. This combination of systematic problem decomposition,
precise mathematical calculation, and solution verification demonstrates the model’s sophisticated
approach to mathematical reasoning tasks requiring spatial visualization.
4.4
Ablation Studies
Effect of Selective Sample Buffer (SSB)
We conducted a comprehensive analysis of the Selective
Sample Buffer (SSB) mechanism and its impact on training efficiency and model performance.
Without SSB, we observed that the percentage of effective training samples (those with non-zero
advantages) decreases substantially during training, limiting the gradient signals available for policy
updates. By implementing SSB, we maintained a consistently high proportion of informative samples
throughout the training process, leading to more stable optimization dynamics and better final
performance.
As shown in Table 2, the SSB mechanism significantly improves model performance on key bench-
marks. With our SSB approach, we achieved superior results on MMMU (73.6 vs. 73.4) while
maintaining the strong performance on MathVista (74.0). More importantly, the proportion of ef-
fective samples remained above 60% with SSB, compared to below 40% without it. This efficiency
gain demonstrates that SSB effectively addresses the “Vanishing Advantages” problem by ensuring a
gradient-rich learning environment even as model responses converge.
SFT vs MPO vs. Hybrid
We evaluated our proposed Hybrid approach [12] against Mixed
Preference Optimization (MPO) and standard Supervised Fine-Tuning (SFT). Table 3 presents the
comparative results across multiple benchmarks and also reports hallucination rates.
Although a specific configuration achieved peak MMMU performance (74.0), it incurred consistent
slight performance degradation on other benchmarks, leading to its exclusion from the final release.
The table shows that hallucination rate using MPO alone (8.7%) was substantially lower than both
the hybrid MPO+GRPO approach (9.1%) and SFT (12.1%), demonstrating MPO’s strong standalone
performance in reducing factual errors. On complex mathematical reasoning tasks, MPO achieved
highest results on AIME 2024 (79.0) and competitive performance on OlympiadBench (60.6),
significantly outperforming SFT (70.0 and 54.6, respectively). When combined with GRPO, our
hybrid approach further improves generalization, achieving the highest scores on OlympiadBench
(62.6) and near-optimal performance on AIME 2024(78.9). This confirms that hybrid approach
11
Table 3: Comparison of different optimization strategies.
Method
Hallucination
MMMU
MathVista
Olympiad
AIME 2024
Rate
Bench
SFT
12.1%
71.6
72.5
54.6
70.0
MPO
8.7%
73.2
73.5
60.6
79.0
MPO+GRPO (Ours)
9.1%
73.6
74.0
62.6
78.9
Table 4: Performance with different component activation configurations.
Configuration
MMMU
MathVista
OlympiadBench
LLM + Adapter
72.1
72.0
60.8
Adapter + Vision encoder
72.3
71.8
60.2
Adapter only
73.6
74.0
62.6
successfully balances reasoning capability with generalization, addressing a key challenge faced
by prior methods. Notably, SFT degraded model reasoning capabilities (as evidenced by its poor
mathematical task performance), which motivated its complete removal from our subsequent training
phases.
Component Activation Analysis
We examined different component activation configurations
to understand their relative contributions to model performance. Our experiments varied which
components were trainable during the optimization process: adapter-only, language model (LLM)
+ adapter, or adapter + vision encoder. The results, presented in Table 4, reveal several interesting
insights about the architecture’s dynamics.
Adapter-only training delivered the best results across all benchmarks (73.6 on MMMU, 74.0 on
MathVista, and 62.6 on OlympiadBench), while LLM + adapter and adapter + vision encoder
configurations yielded lower performance. Somewhat surprisingly, activating the vision encoder
during training provided minimal additional benefits compared to adapter-only training, suggesting
that the primary gains come from improving the alignment between visual features and language
processing rather than enhancing the visual encoding itself. These findings indicate that capabilities
in text and vision are highly transferable—training one modality can directly benefit the other—and
provide valuable guidance for future architectural optimizations.
MPO Threshold Analysis
We analyzed the impact of different MPO threshold values on model
performance across training iterations. With a threshold of 15, we observed more stable training
dynamics compared to lower thresholds. As shown in our experiments, the model achieved 73.3% on
MathVista and 72.6% on MMMU after 420 iterations, compared to 70.4% and 67.8% respectively
with a threshold of 7 at iteration 2420. This indicates that higher thresholds lead to more selective
and effective preference learning, resulting in better final performance with fewer iterations.
The threshold analysis revealed an interesting pattern of initial performance improvement followed
by degradation with lower thresholds. With threshold 10, performance on MMMU peaked at iteration
750 with 73.6% accuracy but declined to 68.9% by iteration 2420. In contrast, higher thresholds
maintained more consistent performance throughout training. This phenomenon aligns with our
observation regarding reward hacking in iterative DPO, where excessive reinforcement can lead to
overfitting to reward signals at the expense of generalization capability.
5
Conclusion and Future Work
We have presented Skywork R1V2, a next-generation multimodal reasoning model that addresses
the fundamental challenge of balancing specialized reasoning capabilities with broad generalization.
Through our novel hybrid reinforcement learning approach combining GRPO, SSB, and MPO, R1V2
achieves significant improvements across multiple reasoning and visual benchmarks, setting new
standards for open-source multimodal models.
12
Mixed Preference Optimization (MPO) strengthens the R1V model’s alignment via the Skywork-VL
reward model [40], effectively reducing repetitive reasoning while maintaining robust generalization.
To address the challenge of “Vanishing Advantages” in GRPO training, we introduce the Selective
Sample Buffer (SSB), which strategically retains high-quality examples with distinct advantage
signals, enabling stable policy updates via gradient-rich training samples.
Our findings highlight an important trade-off between reasoning capability and visual hallucination,
underscoring the need for careful reward calibration during reinforcement learning. This observation
provides valuable guidance for future research in multimodal model development.
Skywork R1V2 establishes new open-source baselines with performance scores of 62.6% on
OlympiadBench, 78.9% on AIME2024, 63.6% on LiveCodeBench, and 73.6% on MMMU. These
results not only outperform existing open-source models but also substantially reduce the gap with
proprietary state-of-the-art systems.
In future work, we plan to explore more sophisticated integration mechanisms between visual and
textual modalities, further refine the balance between reasoning and generalization, and extend our
hybrid reinforcement learning approach to additional domains and modalities.
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