Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
Authors: name: Chris; name: Yichen Wei; name: Yi Peng; name: Xiaokun Wang; name: Weijie Qiu; name: Wei Shen; name: Tianyidan Xie; name: Jiangbo Pei; name: Jianhao Zhang; name: Yunzhuo Hao; name: Xuchen Song; name: Yang Liu; name: Yahui Zhou
Published: 2025-04-23 12:24:10

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 harmonizes
reward-model guidance with rule-based strategies, thereby addressing the
long-standing challenge of balancing sophisticated reasoning capabilities with
broad generalization. To further enhance training efficiency, we propose the
Selective Sample Buffer (SSB) mechanism, which effectively counters the
``Vanishing Advantages'' dilemma inherent in Group Relative Policy Optimization
(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 throughout the training process. Empirical results
affirm the exceptional capability of R1V2, with benchmark-leading performances
such as 62.6 on OlympiadBench, 79.0 on AIME2024, 63.6 on LiveCodeBench, and
74.0 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 systems, including Gemini 2.5 and
OpenAI o4-mini. The Skywork R1V2 model weights have been publicly released to
promote openness and reproducibility
https://huggingface.co/Skywork/Skywork-R1V2-38B.