When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning
This paper identifies a failure mode called Positive-Credit Contamination in RL for LLMs, where low-probability erroneous tokens receive identical positive credit as plausible ones. The proposed TACO method computes a tail-risk score to calibrate credit assignment, outperforming GRPO baselines across three LLMs and eight benchmarks while improving training stability in long-horizon RL.
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[Submitted on 8 Jul 2026]
Title:When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning
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Abstract:Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely used critic-free RL methods rely on uniform credit assignment, broadcasting the same advantage to all tokens regardless of their differences. We identify a critical failure mode of this design, which we refer to as Positive-Credit Contamination: low-probability tail tokens that are contextually erroneous receive identical positive credit to plausible ones within the same trajectory, resulting in the indiscriminate reinforcement of flawed reasoning behavior. To mitigate this issue, we propose Tail-Aware Credit calibratiOn (TACO), a method that calibrates uniform credit assignment to suppress undesirable positive updates. TACO first computes a tail-risk score that incorporates the local generation context to assess each token's risk of falling into the unreliable tail, distinguishing unexpected rarity from uncertainty-driven exploration. TACO then uses this score to tune positive credit for risky tokens without removing their gradients entirely, so that recurring useful rare patterns can accumulate reinforcement while incidental noise is progressively dampened. Experimental results across three LLMs and eight benchmarks show that TACO consistently outperforms GRPO-style baselines. Notably, TACO improves training stability, supporting sustained performance gains in long-horizon RL. The source code is available at: this https URL.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.07976 [cs.CL]
(or arXiv:2607.07976v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.07976
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zicheng Xu [view email] [v1] Wed, 8 Jul 2026 23:00:54 UTC (1,901 KB)
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