QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning

Abstract:GRPO-style reinforcement learning (RL)-based LLM fine-tuning algorithms have recently gained popularity. Relying on heuristic trust-region approximations, however, they can lead to brittle optimization behavior, as global importance-ratio clipping and group-wise normalization fail to regulate samples whose importance ratios fall outside the clipping range. We propose Query-Adaptive Trust-Region policy Optimization (QUATRO), which directly enforces trust-region constraints through a principled optimization. This yields a clear and interpretable objective that enables explicit control over policy updates and stable, entropy-controlled optimization, with a stabilizer terms arising intrinsically from the exact trust-region formulation. Empirically verified on diverse mathematical reasoning benchmarks, QUATRO shows stable training under increased policy staleness and aggressive learning rates, maintaining well-controlled entropy throughout training.

Machine Learning (cs.LG)

arXiv:2602.04620 [cs.LG]

(or arXiv:2602.04620v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.04620 Focus to learn more arXiv-issued DOI via DataCite

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