Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning | ScienceToStartup | ScienceToStartup

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References (25)

[1]

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards

2025Xinyu Tang, Yu-Liang Zhan et al.

[2]

CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning

2025Shijie Zhang, Guohao Sun et al.

[3]

Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

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Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

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Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

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