Papers by Muyun Ding

1 papers
RiT: Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models (2026.findings-acl)

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Challenge: Large Reasoning Models benefit from generating intermediate reasoning steps alongside final answers.
Approach: They propose a framework to introduce thinking-rubric supervision into intermediate reasoning.
Outcome: The proposed framework outperforms outcome-only RL baselines on reasoning-intensive and open-ended tasks.

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