# Meta-RL Induces Exploration in Language Agents arXiv: https://arxiv.org/abs/2512.16848 ยท HTML: https://mlsanity.com/paper/2512.16848/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. - Builds on 46 of its 57 references that are in the map. - First posted on arXiv 18 Dec 2025. ## Placed in - LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/) - Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/) ## Builds on (most cited first, 30 of 46) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023) - [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024) - [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025) - [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023) - [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022) - [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020) - [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020) - [STaR: Bootstrapping Reasoning With Reasoning](https://mlsanity.com/paper/2203.14465/) (2022) - [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021) - [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016) - [Group-in-Group Policy Optimization for LLM Agent Training](https://mlsanity.com/paper/2505.10978/) (2025) - [Training Language Models to Self-Correct via Reinforcement Learning](https://mlsanity.com/paper/2409.12917/) (2024) - [RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2504.20073/) (2025) - [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016) - [ReFT: Reasoning with Reinforced Fine-Tuning](https://mlsanity.com/paper/2401.08967/) (2024) - [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://mlsanity.com/paper/2509.02547/) (2025) ## Built on by (most cited first, 21 of 21) - [Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning](https://mlsanity.com/paper/2605.06130/) (2026) - [RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback](https://mlsanity.com/paper/2603.08561/) (2026) - [Meta-Reinforcement Learning with Self-Reflection for Agentic Search](https://mlsanity.com/paper/2603.11327/) (2026) - [Test-time Recursive Thinking: Self-Improvement without External Feedback](https://mlsanity.com/paper/2602.03094/) (2026) - [AdaMEM: Test-Time Adaptive Memory for Language Agents](https://mlsanity.com/paper/2606.05684/) (2026) - [SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution](https://mlsanity.com/paper/2607.26784/) (2026) - [MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation](https://mlsanity.com/paper/2603.03680/) (2026) - [Meta-TTL: Meta-Learning Self-Improvement Policies for Language Agents](https://mlsanity.com/paper/2604.00830/) (2026) - [Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry](https://mlsanity.com/paper/2607.03702/) (2026) - [LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure](https://mlsanity.com/paper/2606.15306/) (2026) - [Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning](https://mlsanity.com/paper/2606.21943/) (2026) - [Training Language Agents to Learn from Experience](https://mlsanity.com/paper/2605.20477/) (2026) - [Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents](https://mlsanity.com/paper/2609.02042/) (2026) - [Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents](https://mlsanity.com/paper/2604.11465/) (2026) - [Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning](https://mlsanity.com/paper/2605.27960/) (2026) - [Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning](https://mlsanity.com/paper/2606.20002/) (2026) - [CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution](https://mlsanity.com/paper/2609.04865/) (2026) - [Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents](https://mlsanity.com/paper/2609.22120/) (2026) - [From Memory to Guide: Spatio-Temporal Composer for Procedural Coding Memory](https://mlsanity.com/paper/2610.04868/) (2026) - [FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents](https://mlsanity.com/paper/2610.07898/) (2026) - [Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents](https://mlsanity.com/paper/2610.13058/) (2026) ## Abstract (arXiv) Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the RL-trained agents often struggle in tasks that require active exploration and fail to efficiently adapt from trial-and-error experiences. In this paper, we present LaMer, a general Meta-RL framework that enables LLM agents to actively explore and learn from the environment feedback at test time. LaMer consists of two key components: (i) a cross-episode training framework to encourage exploration and long-term rewards optimization; and (ii) in-context policy adaptation via reflection, allowing the agent to adapt their policy from task feedback signal without gradient update. Experiments across diverse environments show that LaMer significantly improves performance over RL baselines, with 11%, 14%, and 19% performance gains on Sokoban, MineSweeper and Webshop, respectively. Moreover, LaMer also demonstrates better generalization to more challenging or previously unseen tasks compared to the RL-trained agents. Overall, our results demonstrate that Meta-RL provides a principled approach to induce exploration in language agents, enabling more robust adaptation to novel environments through learned exploration strategies.