Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
arXiv:2510.00526v3 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) is the standard approach for post-training large language models (LLMs), yet it often shows limited generalization. We trace this limitation to its default training objective: negative log likelihood (NLL). While NLL is classically optimal when training from scratch, post-training operates in a different paradigm and could violate its optimality assumptions, where models already encode task-relevant priors and supervision can be long and noisy. In this work, we systematically study various probability-based
Research continues to push the boundaries of large language model (LLM) performance, and this paper presents a methodological refinement in supervised fine-tuning (SFT) that addresses current generalization limitations.
This research suggests a potential breakthrough in LLM fine-tuning efficiency and effectiveness, critical for broader AI applications and potentially reducing compute requirements for achieving advanced capabilities.
The focus moves beyond traditional negative log likelihood objectives to probability-based methods, indicating a new direction for optimizing LLM performance post-training.
- · AI researchers and developers
- · LLM-dependent industries
- · Developers of specialized AI agents
- · Small and medium AI companies
- · Companies relying on inefficient SFT methods
- · Current compute-intensive fine-tuning approaches
More robust and generalizable LLMs become accessible for a wider range of applications.
The cost of developing and deploying high-performing specialized LLMs could decrease, stimulating innovation across sectors.
Enhanced LLM capabilities could accelerate the development and deployment of sophisticated AI agents, changing workflow automation paradigms.
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