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

• URL: http://arxiv.org/abs/2602.04620v2
• Date: Fri, 06 Feb 2026 13:09:59 GMT
• Title: QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning
• Authors: Doyeon Lee, Eunyi Lyou, Hyunsoo Cho, Sookyung Kim, Joonseok Lee, Jaemoo Choi,
• Abstract summary: We propose Query-Adaptive Trust-Region policy optimization (QUATRO)<n>QUATRO directly enforces trust-region constraints through a principled optimization.<n> Empirically verified on diverse mathematical reasoning benchmarks, QUATRO shows stable behavior under increased policy staleness.
• Score: 30.908304728142983
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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