Papers with reinforcement learning paradigm
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (D18-1)
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| Challenge: | Experimental results reveal dual structure between OIE and OIN tasks helps to build better OIE agents and OINE agents. |
| Approach: | They propose an Open-Domain Information Narration task as the reverse task of Open Information Extraction (OIE) they then propose an OIN task as an OIE agent and an OIR agent to implement the dual structure . |
| Outcome: | The proposed task is the reverse task of Open Information Extraction (OIE) The proposed system is able to implement the dual structure with a reinforcement learning paradigm. |
Learning to Generalize for Sequential Decision Making (2020.findings-emnlp)
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| Challenge: | Recent advances show strong evidence of generalization in spatiotemporal modalities such as robotic manipulation. |
| Approach: | They propose a method for converting a reinforcement learning model into a natural language understanding model by a teacher-student imitation learning method. |
| Outcome: | The proposed model outperforms teacher performance on held-out decision problems by 7% and 24% on out-of-domain problems. |
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available. |
| Approach: | They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier. |
| Outcome: | The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%. |