Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models
arXiv:2605.29303v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradigm provides a cold-start for RL exploration, avoiding the inefficiency of pure RL where on-policy sampling yields insufficient positive samples. However, in practice, existing approaches often use a small amount of data for SFT initialization compared to the RL phase, which can cause the model to fit the limited samples and shift away from its pre-trained distribution. This distribution shift impe
This research addresses a critical challenge in current large language model fine-tuning, where SFT's limitations are becoming more apparent with increasing model scale and application diversity.
Improved fine-tuning methodologies lead to more robust and reliable AI models, critical for deployment across various strategic applications and reducing the risk of model degradation over time.
The proposed 'Token Masking' technique offers a more efficient and stable fine-tuning process, potentially mitigating distribution shift and leading to better-performing LLMs without over-fitting to limited SFT data.
- · AI developers
- · Cloud AI providers
- · Enterprises leveraging LLMs
- · AI research institutions
- · Inefficient LLM fine-tuning methods
- · Developers struggling with distribution shift
More accurate and stable large language models become available for various applications.
Reduced computational costs and time for advanced model development due to more efficient fine-tuning.
Accelerated deployment of highly specialized AI agents and applications across industries by lowering the barrier to robust customization.
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Read at arXiv cs.AI