Papers with meta-reinforcement learning approach
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level. |
| Approach: | They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. |
| Outcome: | The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set. |
Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects . |
| Approach: | MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. |
| Outcome: | MetaJuLS achieves 1.5-2.0 speedups over GPU-optimized baselines while maintaining accuracy within 0.2% of state-of-the-art parsers. |