Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning

• URL: http://arxiv.org/abs/2512.12690v1
• Date: Sun, 14 Dec 2025 13:46:42 GMT
• Title: Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
• Authors: Yongcan Yu, Lingxiao He, Shuo Lu, Lijun Sheng, Yinuo Xu, Yanbo Wang, Kuangpu Guo, Jianjie Cheng, Meng Wang, Qianlong Xie, Xingxing Wang, Dapeng Hu, Jian Liang,
• Abstract summary: SFT plays a crucial role across several scenarios.<n>SFT with only 2K achieves comparable or better reasoning performance to RL with 20K.<n>We identify a pervasive issue of deceptive rewards, where higher rewards fail to correlate with better reasoning accuracy in RL.
• Score: 30.751908700207185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away from the supervised fine-tuning (SFT) paradigm. Many studies suggest that introducing the SFT stage not only fails to improve reasoning ability but may also negatively impact model training. In this study, we revisit this RL-centric belief through a systematic and controlled comparison of SFT and RL on VLM Reasoning. Using identical data sources, we find that the relative effectiveness of SFT and RL is conditional and strongly influenced by model capacity, data scale, and data distribution. Contrary to common assumptions, our findings show that SFT plays a crucial role across several scenarios: (1) Effectiveness for weaker models. SFT more reliably elicits reasoning capabilities in smaller or weaker VLMs. (2) Data efficiency. SFT with only 2K achieves comparable or better reasoning performance to RL with 20K. (3) Cross-modal transferability. SFT demonstrates stronger generalization across modalities. Moreover, we identify a pervasive issue of deceptive rewards, where higher rewards fail to correlate with better reasoning accuracy in RL. These results challenge the prevailing "RL over SFT" narrative. They highlight that the role of SFT may have been underestimated and support a more balanced post-training pipeline in which SFT and RL function as complementary components.

Related papers

• Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning [8.550698116833123]
  Post-training of reasoning LLMs typically consists of an offline SFT stage followed by an online reinforcement learning stage.<n>We show that, after identical RL training, models from stronger SFT checkpoints can significantly underperform those from weaker ones.<n>We propose PEAR, an SFT-stage method that corrects this mismatch and better prepares the model for RL.
  arXiv  Detail & Related papers  (2026-02-01T06:53:45Z)
• Trust-Region Adaptive Policy Optimization [82.09255251747818]
  Post-training methods play an important role in improving large language models' (LLMs) complex reasoning abilities.<n>We introduce TRAPO, a framework that interleavesSupervised Fine-Tuning (SFT) and Reinforcement Learning (RL) within each training instance.<n>Experiments on five mathematical reasoning benchmarks show that TRAPO consistently surpasses standard SFT, RL, and SFT-then-RL pipelines.
  arXiv  Detail & Related papers  (2025-12-19T14:37:07Z)
• Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking [56.46309219272326]
  For large language models (LLMs), classification via supervised fine-tuning (SFT) predicts ''yes'' (resp. ''no'') token for relevant (resp. irrelevant) pairs.<n>This divergence raises a central question: which objective is intrinsically better suited to LLM-based reranking, and what mechanism underlies the difference?<n>We conduct a comprehensive comparison and analysis between CL and SFT for reranking, taking the universal multimodal retrieval (UMR) as the experimental playground.
  arXiv  Detail & Related papers  (2025-10-16T16:02:27Z)
• Mitigating Forgetting Between Supervised and Reinforcement Learning Yields Stronger Reasoners [28.039145840787683]
  Supervised fine-tuning (SFT) offers complementary benefits but typically requires large-scale data and risks overfitting.<n>Recent attempts to combine SFT and RL face three main challenges: data inefficiency, algorithm-specific designs, and catastrophic forgetting.<n>We propose a plug-and-play framework that dynamically integrates SFT into RL by selecting challenging examples for SFT.
  arXiv  Detail & Related papers  (2025-10-06T03:01:14Z)
• Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead [20.446287312285648]
  We study whether high SFT scores translate to improved performance after RL.<n>We find high SFT scores can be biased toward simpler or more homogeneous data and are not reliably predictive of subsequent RL gains or scaled-up post-training effectiveness.<n>We study alternative metrics and identify generalization loss on held-out reasoning examples and Pass@large k performance to provide strong proxies for the RL outcome.
  arXiv  Detail & Related papers  (2025-10-02T02:57:00Z)
• RL Fine-Tuning Heals OOD Forgetting in SFT [35.01074051556079]
  We investigate the evolution and mechanism behind the synergy ofSupervised Fine-Tuning and Reinforcement Learning.<n>Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism.
  arXiv  Detail & Related papers  (2025-09-08T21:40:41Z)
• The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs [66.17068546293487]
  Large vision-language models (VLMs) increasingly adopt post-training techniques such as long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL) to elicit sophisticated reasoning.<n>We present a systematic investigation into the distinct roles and interplay of long-CoT SFT and RL across multiple multimodal reasoning benchmarks.<n>We find that SFT improves performance on difficult questions by in-depth, structured reasoning, but introduces verbosity and degrades performance on simpler ones.
  arXiv  Detail & Related papers  (2025-07-10T09:05:49Z)
• Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective [98.45690529036848]
  Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt multimodal large language models to downstream tasks.<n>While effective at task adaptation, their impact on prior knowledge remains unclear.
  arXiv  Detail & Related papers  (2025-06-30T04:15:01Z)
• AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy [48.30596996677882]
  We investigate the synergy between supervised fine-tuning (SFT) and reinforcement learning (RL) in developing strong reasoning models.<n> scaling strategies yield notable improvements in reasoning performance.<n>Our AceReason-Nemotron-1.1 7B model significantly outperforms AceReason-Nemotron-1.0 and new state-of-the-art performance among Qwen2.5-7B-based reasoning models.
  arXiv  Detail & Related papers  (2025-06-16T09:27:48Z)
• SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models [39.551767637896404]
  This work revisits the dominant supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm for training Large Vision-Language Models (LVLMs)<n>We show that SFT can significantly undermine subsequent RL by inducing pseudo reasoning paths'' imitated from expert models.<n>We introduce VLAA-Thinking, a new multimodal dataset designed to support reasoning in LVLMs.
  arXiv  Detail & Related papers  (2025-04-10T16:54:05Z)
• OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles [91.88062410741833]
  We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning.<n>We show that OpenVLThinker-7B consistently advances performance across six benchmarks demanding mathematical and general reasoning.
  arXiv  Detail & Related papers  (2025-03-21T17:52:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.