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EnvRL Framework Boosts LLM Agent Performance in Complex Tasks

Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li· June 17, 2026 View original

Summary

A new framework called EnvRL enhances agentic reinforcement learning for Large Language Models by incorporating environment dynamics learning. It uses auxiliary objectives like state prediction and inverse dynamics to help agents internalize environment mechanisms, leading to significant improvements in success rates on long-horizon tasks.

This research introduces EnvRL, a novel framework designed to improve the performance of Large Language Models (LLMs) when operating as agents in complex, long-horizon tasks. Traditional reinforcement learning (RL) often struggles with sparse rewards in such scenarios, overlooking valuable information embedded in the agent's interactions with its environment. EnvRL addresses this by treating interaction experience as an implicit supervisory signal, allowing the agent to build a more accurate internal model of the environment's dynamics. The framework integrates two auxiliary objectives—state prediction and inverse dynamics—which are optimized alongside the primary RL objective. Experimental results on benchmarks like ALFWorld and WebShop demonstrate that EnvRL significantly boosts success rates compared to RL-only baselines. For instance, it improved a Qwen-2.5-1.5B-Instruct model's success rate from 72.8% to 77.4% on ALFWorld, showcasing its potential for more robust and effective AI agents.

Why it matters

Professionals developing AI agents for complex, multi-step tasks can use EnvRL to overcome challenges posed by sparse rewards and improve agent reliability and performance. This approach can lead to more capable and autonomous AI systems.

How to implement this in your domain

  1. 1Integrate environment dynamics learning into existing LLM agent training pipelines.
  2. 2Experiment with state prediction and inverse dynamics as auxiliary objectives in reinforcement learning setups.
  3. 3Apply EnvRL to long-horizon agentic tasks where sparse rewards are a common issue.
  4. 4Evaluate the impact of internalizing environment dynamics on agent success rates and robustness.

Who benefits

AI DevelopmentRoboticsGamingCustomer Service Automation

Key takeaways

  • EnvRL improves LLM agents by leveraging environment dynamics as an implicit supervision signal.
  • Auxiliary objectives like state prediction and inverse dynamics enhance agent's internal environment models.
  • The framework significantly boosts success rates in long-horizon agentic tasks with sparse rewards.
  • EnvRL offers a path to more robust and capable AI agents for complex operations.

▶ The 60-second brief

Original post by Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li

"arXiv:2606.17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents. However, conventional RL methods for long-horizon agentic tasks often struggle with sparse outcome rewards. Intuitiv…"

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Originally posted by Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li on X · view source

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