Papers with training agents
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR (2026.eacl-long)
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James Burgess, Jan N. Hansen, Duo Peng, Yuhui Zhang, Alejandro Lozano, Min Woo Sun, Emma Lundberg, Serena Yeung-Levy
| Challenge: | Recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). |
| Approach: | They propose to train search agents to search and reason over scientific papers and a factoid QA dataset with 60k biomedical paper abstracts. |
| Outcome: | The proposed model outperforms non-RL retrieval baselines and is scalable and extendable to other scientific domains. |
Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Existing methods to improve persona consistency are centered around supervised learning or online reinforcement learning (RL). Existing approaches to improve consistency are expensive and require additional training. |
| Approach: | They propose an offline supervised learning framework to improve persona consistency of dialogue systems by punishing and rewarding specific utterances. |
| Outcome: | The proposed framework improves both the persona consistency and dialogue quality of a state-of-the-art social chatbot. |