Sitong Wu The Chinese University of Hong Kong, Haoru Tan, Xichen Zhang Hong Kong University of Science and Technology, Bin Xia, Wenhu Zhang, Xiaojuan Qi, Bei Yu Department of Computer Science and Engineering, The Chinese University of Hong Kong, Jiaya Jia Department of Computer Science and Engineering, Hong Kong University of Science and Technology
Reinforcement Learning (RL) with sparse outcome rewards suffers from inefficient credit assignment in complex LLM reasoning tasks. While utilizing stronger LLMs as teachers to derive dense token-level supervision offers a cost-effective alternative to proprietary reward models, it relies on the flawed assumption that teachers are perfect oracles. In reality, teacher models exhibit capability limitations and uncertainty, producing noisy signals that make student policies susceptible to reward hacking. To address this, we propose Teacher Reward Adaptive Calibration (TRAC), a robust framework that filters noisy supervision by dynamically modulating teacher influence via a multi-granularity calibration mechanism. TRAC evaluates teacher reliability across three principled dimensions: problem-level expertise, trajectory-level discrimination, and token-level confidence. Furthermore, we integrate TRAC with Group Relative Policy Optimization (GRPO), formulating as TRAC-GRPO, which treats calibrated teacher-derived reward as an additive advantage reshaping term to ensure fair advantage estimation. Extensive experiments demonstrate that TRAC effectively mitigates teacher noise, significantly enhancing the reasoning capabilities and training stability of LLMs compared to standard baselines. The code will be available at: https://github.com/JIA-Lab-research/TRAC.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xichen Zhang Hong Kong University of Science and Technology, Ziyi He, Yinghao Zhu The University of Hong Kong, Sitong Wu The Chinese University of Hong Kong, Shaozuo Yu, Meng Chu, Wenhu Zhang, Haoru Tan 等 9 位作者
Search agents have emerged as a pivotal paradigm for solving open-ended, knowledge-intensive reasoning tasks. However, training these agents via Reinforcement Learning (RL) faces a critical dilemma: interacting with live commercial Web APIs is prohibitively expensive, while relying on static data snapshots often introduces noise due to data misalignment. This misalignment generates corrupted reward signals that destabilize training by penalizing correct reasoning or rewarding hallucination. To address this, we propose SearchGym, a simulation environment designed to bootstrap robust search agents. SearchGym employs a rigorous generative pipeline to construct a verifiable knowledge graph and an aligned document corpus, ensuring that every reasoning task is factually grounded and strictly solvable. Building on this controllable environment, we introduce SearchGym-RL, a curriculum learning methodology that progressively optimizes agent policies through purified feedback, evolving from basic interactions to complex, long-horizon planning. Extensive experiments across the Llama and Qwen families demonstrate strong Sim-to-Real generalization. Notably, our Qwen2.5-7B-Base model trained within SearchGym surpasses the web-enhanced ASearcher baseline across nine diverse benchmarks by an average relative margin of 10.6%. Our results validate that high-fidelity simulation serves as a scalable and highly cost-effective methodology for developing capable search agents.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表