Papers with reinforcement learning (RL) framework
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems (2022.findings-naacl)
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| Challenge: | Existing models for persuasive dialogue lack emotion annotated data, so we use transformers to provide emotion based feedbacks to our RL agent. |
| Approach: | They propose to use a language model to generate empathetic persuasive dialogues . they annotate existing data with emotions and build transformers to provide feedbacks based on emotion. |
| Outcome: | The proposed model increases the rate of generating persuasive responses compared to state-of-the-art models while maintaining the language quality. |
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation (2026.eacl-long)
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| Challenge: | Existing approaches to generate Boolean queries for systematic reviews are limited by the lack of ground-truth best Boolesan queries. |
| Approach: | They propose a reinforcement learning framework that trains large language models to generate effective Boolean queries for medical systematic reviews. |
| Outcome: | The proposed framework outperforms zero-shot/few-shot prompting on 65 588 topics . it also matches or exceeds the effectiveness of larger GPT-based models using smaller backbones . |
Reinforced Training Data Selection for Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold. |
| Approach: | They propose a framework that searches for training instances relevant to the target domain and learns better representations for them. |
| Outcome: | The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks. |
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in machine translation, but most MT-specific LLMs rely heavily on external supervision during training. |
| Approach: | They propose a reinforcement learning framework for machine translation that is reference-free and relies solely on self-judging rewards. |
| Outcome: | The proposed framework outperforms existing LLMs and larger general LLM models on English Chinese translation benchmarks and performs competitively with leading closed-source systems. |
Chatbot To Help Patients Understand Their Health (2025.findings-emnlp)
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Won Seok Jang, Hieu Tran, Manav Shaileshkumar Mistry, Sai Kiran Gandluri, Yifan Zhang, Sharmin Sultana, Sunjae Kwon, Yuan Zhang, Zonghai Yao, Hong Yu
| Challenge: | NoteAid-Chatbot is a conversational AI designed to help patients better understand their health . |
| Approach: | They propose a new learning paradigm that leverages a multi-agent large language model and reinforcement learning framework without relying on costly human-generated training data. |
| Outcome: | The proposed framework surpasses non-expert human training methods. |
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
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Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
| Approach: | They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents. |
| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)
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Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He
| Challenge: | Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction. |
| Approach: | They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness. |
| Outcome: | The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks. |
TA-GRPO-d: Trajectory-Aware GRPO for Optimizing Denoising Trajectories in Diffusion LLMs (2026.acl-long)
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| Challenge: | Existing dLLMs rely on fixed denoising schedules and cannot learn efficient unmasking orders. |
| Approach: | They propose a framework that transforms dLLM decoding into a trajectory-aware policy . it uses a confidence-gated denoising strategy that decides which tokens to unmask . |
| Outcome: | The proposed model can learn which tokens to unmask and how many to unmak per step . it can learn the output quality and efficiency of the decoding path itself . |
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing iterative refinement strategies that generate solutions in a single forward pass often hit a performance ceiling on complex algorithmic tasks. |
| Approach: | They propose a reinforcement learning framework that internalizes the structured reasoning trajectory directly into the model’s weights. |
| Outcome: | The proposed framework achieves 94.51% (87.20%) on HumanEval, 81.80% (78.57%) on MBPP, 35.00% on BigCodeBench, 52.21% on LiveCodeBech, and 37.34% on CodeForces in a single-attempt setting. |
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning (2026.acl-long)
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| Challenge: | Prior approaches to rewriting large language models shatters linguistic coherence and removes privacy-sensitive information. |
| Approach: | They propose a framework that trains an agent to dynamically route text chunks . it implicitly distinguishes between replaceable Personally Identifiable Information (PII) and task-critical PII . |
| Outcome: | The proposed framework achieves state-of-the-art on the privacy-utility frontier . it trains an agent to dynamically route text chunks, learning a policy that balances privacy leakage and task performance. |