Papers with RL task
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning (P18-1)
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| Challenge: | Recent work has shown that reinforcement learning (RL) can be scaled to games with large state-action spaces, achieving human-level performance or even superhuman performance. |
| Approach: | They propose to use bandit feedback to improve sequence-to-sequence learning by simulating reward signals by evaluation metrics such as BLEU, F1-score, or ROUGE. |
| Outcome: | The proposed methods improve performance even from small amounts of human feedback, pointing to a great potential for applications at larger scale. |
Process-Supervised Reinforcement Learning for Code Generation (2025.emnlp-main)
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| Challenge: | Existing reinforcement learning strategies based on outcome supervision have shown effectiveness in code generation tasks, but their effectiveness in the field of code generation remains limited. |
| Approach: | They propose a method that uses a teacher model to mutate and refactor statements and a compiler to automatically label them. |
| Outcome: | The proposed method improves performance in complex code generation tasks. |