Youliang Yuan The Chinese University of Hong Kong-Shenzhen, Qiuyang Mang, Jingbang Chen The Chinese University of Hong Kong, Hong Wan, Xiaoyuan Liu Xiaohongshu and The Chinese University of Hong Kong, Shenzhen, Junjielong Xu Tencent and The Chinese University of Hong Kong, Shenzhen, Jen-tse Huang, Wenxuan Wang Renmin University of China 等 10 位作者
In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model’s reasoning ability. This is evidenced by a high incidence of “false positives”—solutions that reach the correct answer through an unsound process.Through a systematic analysis with human verification, we establish a taxonomy of these failure modes, identifying patterns like Miracle Steps—abrupt jumps to a correct output without a valid preceding derivation. Probing experiments suggest that these Miracle Steps are linked to answer-recall shortcuts, including memorization from pretraining, where the model accesses the correct answer independently of its reasoning chain.To mitigate this systemic issue, we introduce the Rubric Reward Model (RRM), a process-oriented reward function that evaluates the entire reasoning trajectory against problem-specific rubrics.The RRM explicitly penalizes logical flaws and encourages rigorous deduction.When integrated into an RL pipeline, RRM-based training consistently outperforms outcome-only supervision across four math benchmarks.Notably, it boosts Verified Pass@1024 on AIME2024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%.Our work demonstrates that rewarding the solution process is crucial for building accurate and reliable models.
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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
Kangning Zhang, Weiwen Liu Shanghai Jiao Tong University, Wenxiang Jiao, Kounianhua Du Shanghai Jiaotong University, Yuan Lu Xiaohongshu, Weinan Zhang Shanghai Jiaotong University, Yong Yu Shanghai Jiaotong University
Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines, where data generation and model training are executed as two separate, non-interactive processes. This approach fails to focus on the model’s specific weaknesses adaptively and allows noisy labels to persist, degrading training efficiency. We introduce LoopTool, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively evolves both the data and the model through three synergistic modules: (1) Greedy Capability Probing (GCP) diagnoses the model’s mastered and failed capabilities; (2) Judgement-Guided Label Verification (JGLV) uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) Error-Driven Data Expansion (EDDE) generates new, challenging samples based on identified failures. This closed-loop process is tightly integrated with reinforcement learning training and operates within a cost-efficient, open-source ecosystem, thereby eliminating reliance on costly APIs. Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs.
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