ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large
  Vision-and-Language Models
Typical post-training paradigms for Large Vision-and-Language Models (LVLMs)
include Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable
Rewards (RLVR). SFT leverages external guidance to inject new knowledge,
whereas RLVR utilizes internal reinforcement to enhance reasoning capabilities
and overall performance. However, our analysis reveals that SFT often leads to
sub-optimal performance, while RLVR struggles with tasks that exceed the
model's internal knowledge base. To address these limitations, we propose
ViSurf (Visual Supervised-and-Reinforcement
Fine-Tuning), a unified post-training paradigm that integrates the
strengths of both SFT and RLVR within a single stage. We analyze the derivation
of the SFT and RLVR objectives to establish the ViSurf objective, providing a
unified perspective on these two paradigms. The core of ViSurf involves
injecting ground-truth labels into the RLVR rollouts, thereby providing
simultaneous external supervision and internal reinforcement. Furthermore, we
introduce three novel reward control strategies to stabilize and optimize the
training process. Extensive experiments across several diverse benchmarks
demonstrate the effectiveness of ViSurf, outperforming both individual SFT,
RLVR, and two-stage SFT \textrightarrow RLVR. In-depth analysis corroborates
these findings, validating the derivation and design principles of ViSurf.
68ee57ab9b77b5223f666238
Yuqi Liu
claimed_verified
2025-10-14T14:23:53.460Z
669cefd6119595d21b55a995
/avatars/bafc2387ee70b263bf45c42159381da8.svg
Yuqi Liu
user
Ricky06662
68ee57ab9b77b5223f666239
Liangyu Chen
68ee57ab9b77b5223f66623a
Jiazhen Liu
68ee57ab9b77b5223f66623b
Mingkang Zhu
68ee57ab9b77b5223f66623c
Zhisheng Zhong
68ee57ab9b77b5223f66623d
Bei Yu
68ee57ab9b77b5223f66623e
Jiaya Jia
ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large
  Vision-and-Language Models
Typical post-training paradigms for Large Vision-and-Language Models (LVLMs)
include Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable
Rewards (RLVR). SFT leverages external guidance to inject new knowledge,
whereas RLVR utilizes internal reinforcement to enhance reasoning capabilities
and overall performance. However, our analysis reveals that SFT often leads to
sub-optimal performance, while RLVR struggles with tasks that exceed the
model's internal knowledge base. To address these limitations, we propose
ViSurf (Visual Supervised-and-Reinforcement
Fine-Tuning), a unified post-training paradigm that integrates the
strengths of both SFT and RLVR within a single stage. We analyze the derivation
of the SFT and RLVR objectives to establish the ViSurf objective, providing a
unified perspective on these two paradigms. The core of ViSurf involves
injecting ground-truth labels into the RLVR rollouts, thereby providing
simultaneous external supervision and internal reinforcement. Furthermore, we
introduce three novel reward control strategies to stabilize and optimize the
training process. Extensive experiments across several diverse benchmarks
demonstrate the effectiveness of ViSurf, outperforming both individual SFT,
RLVR, and two-stage SFT \textrightarrow RLVR. In-depth analysis corroborates
these findings, validating the derivation and design principles of ViSurf.
ViSurf combines Supervised Fine-Tuning and Reinforcement Learning with Verifiable Rewards to enhance Large Vision-and-Language Models, outperforming individual methods and two-stage approaches.
Supervised Fine-Tuning Reinforcement Learning with Verifiable Rewards Large Vision-and-Language Models ground-truth labels RLVR rollouts reward control strategies