Papers by Clive Bai
Think Outside the Policy: In-Context Steered Policy Optimization (2026.findings-acl)
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| Challenge: | Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods exhibit limited exploration due to reliance on on-policy rollouts which are limited to the current policy’s distribution, resulting in narrow trajectory diversity. |
| Approach: | They propose a framework that leverages the in-context learning capability of Large Reasoning Models to provide expert guidance using existing datasets. |
| Outcome: | The proposed framework improves RLVR performance and training stability on mathematical reasoning benchmarks. |
Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error (2026.acl-long)
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| Challenge: | Existing approaches to RLVR train LMs based on their own on-policy responses and are constrained by the initial capability of LM. |
| Approach: | They propose an approach that hints LMs with their self-made mistakes without external guidance. |
| Outcome: | The proposed approach outperforms the normal group relative policy optimization and requires no external guidance. |