EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning

2026-06-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Natural Language Processing · Depth: Expert, quick

Summary

EnvRL is a novel framework designed to enhance agentic Reinforcement Learning (RL) for Large Language Models (LLMs) tackling long-horizon tasks. It addresses the common challenge of sparse outcome rewards in conventional RL by integrating environment dynamics learning. EnvRL achieves this through two auxiliary objectives: state prediction and inverse dynamics, which are optimized alongside the primary RL objective. This joint optimization encourages the LLM agent to internalize the environment's transition mechanisms from its interaction experiences, thereby constructing a more accurate internal model. Experimental results on two long-horizon agentic benchmarks demonstrate significant improvements. For instance, when trained with GRPO, EnvRL lifted Qwen-2.5-1.5B-Instruct's success rate from 72.8% to 77.4% on ALFWorld and from 56.8% to 67.0% on WebShop, outperforming RL-only baselines.

Key takeaway

For Machine Learning Engineers developing LLM agents for long-horizon tasks with sparse rewards, consider integrating environment dynamics learning. EnvRL's approach, using state prediction and inverse dynamics auxiliary objectives, significantly boosts success rates. You can improve your agent's internal environment model and achieve performance gains like those seen on ALFWorld and WebShop, moving beyond traditional RL-only baselines.

Key insights

EnvRL improves LLM agent performance on long-horizon tasks by learning environment dynamics through state prediction and inverse dynamics.

Principles

• Environment interaction provides implicit supervision.
• Internalizing environment dynamics improves policy learning.
• Sparse outcome rewards can be augmented.

Method

EnvRL jointly optimizes primary RL objectives with two auxiliary objectives: state prediction and inverse dynamics. This encourages agents to internalize environment dynamics from interaction experience.

In practice

• Apply EnvRL to long-horizon agentic tasks.
• Use state prediction and inverse dynamics objectives.
• Improve LLM agent success rates on benchmarks.

Topics

• Reinforcement Learning
• Large Language Models
• Agentic AI
• Environment Dynamics
• State Prediction
• Inverse Dynamics
• Long-Horizon Tasks

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation — LLMs struggle with simultaneous state updates in environment simulation, requiring structured approaches for reliability.
• Reinforcement Learning - Ep. 30 (Deep Learning SIMPLIFIED) — Reinforcement learning enables agents to maximize rewards by learning optimal actions within dynamic, uncertain environments.
• AEL: Agent Evolving Learning for Open-Ended Environments — AEL enables LLM agents to learn from experience by dynamically selecting memory retrieval policies and integrating reflective causal insights.
• EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL — EnvFactory automates environment and trajectory synthesis for Agentic RL, significantly boosting LLM tool-use performance.
• Learning from Language Feedback via Variational Policy Distillation — VPD co-evolves teacher and student policies to overcome sparse rewards in RL via dynamic language feedback interpretation.
• Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application — Agentic environment engineering for LLMs involves systematic modeling, synthesis, evaluation, and co-evolution to enhance model capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.