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3 results for “supervised fine-tuning”
Capacity-Dependent Effects of Data Selection for Reasoning
A new arXiv preprint challenges the assumption that high-likelihood responses are universally optimal for reasoning-focused fine-tuning, demonstrating instead that data selection effectiveness depends critically on model size and training duration.
Aug 17, 2026
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
A new method called Weightless Fine-Tuning (WFT) enables personalization of large language models at decoding time without updating model weights, reducing computational cost while approximating the distributional effect of supervised fine-tuning.
Aug 13, 2026
Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs
A new arXiv preprint claims reinforcement learning (RL) training reduces task conflicts during model merging in LLMs compared to supervised fine-tuning, citing three empirical and theoretical mechanisms.
Jul 27, 2026