Anchored Supervised Fine-Tuning

• URL: http://arxiv.org/abs/2509.23753v2
• Date: Fri, 03 Oct 2025 08:30:05 GMT
• Title: Anchored Supervised Fine-Tuning
• Authors: He Zhu, Junyou Su, Peng Lai, Ren Ma, Wenjia Zhang, Linyi Yang, Guanhua Chen,
• Abstract summary: Post-training of large language models involves a trade-off between supervised fine-tuning and reinforcement learning.<n> Dynamic Fine-Tuning (DFT) recently emerged as a promising middle ground, reweighting SFT objectives with token probabilities.<n>We propose Anchored Supervised Fine-Tuning (ASFT) to augment DFT's reweighting with lightweight KL regularization to preserve tightness while ensuring stability.
• Score: 26.17356786243252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-training of large language models involves a fundamental trade-off between supervised fine-tuning (SFT), which efficiently mimics demonstrations but tends to memorize, and reinforcement learning (RL), which achieves better generalization at higher computational cost. Dynamic Fine-Tuning (DFT) recently emerged as a promising middle ground, reweighting SFT objectives with token probabilities and achieving improvements in certain reasoning domains, though it exhibits instability in other tasks. We provide a analysis of DFT through the reward-weighted regression (RWR) framework, revealing that it corresponds to a specific auxiliary distribution choice that yields provably tighter RL bounds than standard SFT. However, our analysis also uncovers a critical limitation: this construction lacks distributional anchoring, leading to progressive drift that undermines training stability. To address this, we propose Anchored Supervised Fine-Tuning (ASFT), which augments DFT's reweighting with lightweight KL regularization to preserve tightness while ensuring stability. Empirically, ASFT consistently outperforms both SFT and DFT across mathematical reasoning, medical knowledge grounding, and code generation, achieving substantial improvements with minimal computational overhead. Our RWR framework provides a systematic lens for understanding post-training methods and demonstrates that principled theoretical analysis leads to both stronger guarantees and practical gains.

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