Papers with reinforcement learning problem
Reference-Free Schema Generation for Literature Review Tables via Multi-Faceted Rewards (2026.acl-srw)
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| Challenge: | Literature review systems generate literature review tables by inferring schemas and values from documents. |
| Approach: | They propose to use schema generation as a reinforcement learning problem to determine which dimensions to compare a set of papers. |
| Outcome: | The proposed model improves over the untuned model across intrinsic, reference-based, and LLM-judge metrics and remains competitive with supervised fine-tune models at 5 the parameter count on structural and diversity dimensions. |
Learning Task Sampling Policy for Multitask Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to train multi-task models with auxiliary tasks are limited by the number of combinations and the importance of each auxiliary task is not known a priori. |
| Approach: | They propose a search method that automatically assigns importance weights to auxiliary tasks to improve the target task quality. |
| Outcome: | The proposed method outperforms uniform sampling and the corresponding single-task baseline on XNLI and GLUE. |
Thread Popularity Prediction and Tracking with a Permutation-invariant Model (D18-1)
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| Challenge: | a task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. |
| Approach: | They propose a deep neural network architecture to model the expected cumulative reward of a recommendation (action) they employ a greedy procedure to approximate the action that maximizes the predicted Q-value . |
| Outcome: | The proposed approach outperforms the state-of-the-art on five real-world datasets. |
Incorporating Review-missing Interactions for Generative Explainable Recommendation (2025.coling-main)
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| Challenge: | Existing models of explainable recommendation use user reviews as ground truths, but in practice, a large amount of users may not leave reviews after purchasing items. |
| Approach: | They propose to incorporate user preferences into explainable recommender models by leveraging generative models to predict the missing reviews and then training the model based on all the predicted and original reviews. |
| Outcome: | The proposed model improves the explanation quality on three publicly available datasets. |
Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers (2023.findings-acl)
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| Challenge: | In collaborative situations, communication is performed as signalling and recognizing, and the ability to act on these signals is crucial for future machine learning models to collaborate and interact with humans naturally. |
| Approach: | They propose to use a referential language game as an example of a collaborative joint activity to evaluate intra-episodic feedback given by a teacher. |
| Outcome: | The proposed model can generalize on aspects of scene complexity and perform better than providing only the initial statement. |
Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks (2025.emnlp-main)
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| Challenge: | Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks. |
| Approach: | They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. |
| Outcome: | The proposed model outperforms existing models on a BRIGHT benchmark with BM25 retrievers. |
V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment (2026.findings-acl)
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| Challenge: | Current methods for steering large language models rely on prompt engineering or reasoning-time guidance. |
| Approach: | They propose a value-controllable pluralistic alignment framework enhanced with conditioned gating that dynamically directs the flow among multiple experts based on an input value or moral vector. |
| Outcome: | The proposed method outperforms prompt-based steering and multi-task PEFT benchmarks on two 8-billion-parameter backbones. |