Technical Summary: ENVRL - Learn from Environment Dynamics in Agentic Reinforcement Learning

1. Problem Statement

Reinforcement Learning (RL) has become a standard paradigm for training Large Language Models (LLMs) as autonomous agents capable of handling complex, long-horizon tasks (e.g., web navigation, software interaction). However, prevailing agentic RL algorithms (such as GRPO and RLOO) predominantly rely on outcome-based rewards, where the environment provides binary feedback only upon the completion of an episode.

In long-horizon, multi-turn contexts, this sparse feedback poses a severe learning challenge. The authors argue that this approach overlooks the rich environment dynamics information contained within rollout interaction trajectories. While the agent interacts with the environment, it generates experience that implicitly carries dense signals describing how the environment evolves in response to actions and revealing underlying transition mechanisms. Existing methods fail to leverage this implicit supervision, limiting the agent's ability to construct an accurate internal model of the environment and make robust decisions.

2. Methodology: The ENVRL Framework

The paper proposes ENVRL, a framework designed to incorporate environment dynamics learning into agentic RL. The core idea is to transform interaction experiences into self-supervised signals that complement the primary RL objective. ENVRL introduces two auxiliary objectives:

A. State Prediction (SP)

This objective models forward environment dynamics. Given the current state $s_t$ and the chosen action $a_t$, the agent is trained to predict the next environment state $s_{t+1}$.

• Mechanism: In textual environments, $s_{t+1}$ is the next-step textual observation. The objective minimizes the cross-entropy loss between the predicted and actual next state:
  $$L_{SP}(\theta) = -E_{(s_t, a_t, s_{t+1}) \sim D_{\pi_\theta}} [\log p_\theta(s_{t+1} | s_t, a_t)]$$
• Goal: Encourages the agent to internalize how its actions shape subsequent observations.

B. Inverse Dynamics (ID)

This objective models backward causality. Given two consecutive states $(s_t, s_{t+1})$, the agent is trained to infer the action $a_t$ that caused the transition.

• Mechanism: The objective minimizes the cross-entropy loss for recovering the action:
  $$L_{ID}(\theta) = -E_{(s_t, a_t, s_{t+1}) \sim D_{\pi_\theta}} [\log p_\theta(a_t | s_t, s_{t+1})]$$
• Goal: Strengthens the agent's understanding of the causal link between its behavior and environmental changes, helping it filter out extraneous observation details.

C. Joint Online Optimization with Decay

The total learning objective combines the primary RL loss ($L_{RL}$) with the auxiliary losses ($L_{SP}$ and $L_{ID}$):
$$L(\theta; t) = L_{RL}(\theta) + \lambda_{SP}(t) L_{SP}(\theta) + \lambda_{ID}(t) L_{ID}(\theta)$$

To balance the need for early dynamics learning with late-stage reward maximization, the authors employ a coefficient decay mechanism. The weights $\lambda_{SP}(t)$ and $\lambda_{ID}(t)$ follow a cosine decay schedule, starting at a higher value and gradually reducing to zero as training progresses. This ensures the agent receives dense supervision on dynamics early on, then smoothly shifts focus to the primary task reward.

Computational Efficiency: ENVRL reuses rollout data generated during the standard RL process. It requires only a single additional forward pass per update to compute the auxiliary losses, introducing negligible computational overhead.

3. Key Contributions

1. Framework Proposal: Introduction of ENVRL, a lightweight framework that integrates state prediction and inverse dynamics as auxiliary objectives into agentic RL.
2. Implicit Supervision Utilization: A novel approach to treating interaction experience as an independent source of dense supervision signals, addressing the sparsity of outcome rewards in long-horizon tasks.
3. Decay Mechanism: A time-dependent coefficient schedule that dynamically balances the learning of environment dynamics against the optimization of task rewards.
4. Efficiency: Demonstration that these improvements are achieved without additional sampling or separate model training, reusing existing rollout trajectories.

4. Experimental Results

The authors evaluated ENVRL on two long-horizon agentic benchmarks: ALFWorld (text-based household tasks) and WebShop (e-commerce product search). Experiments were conducted using Qwen2.5 models (1.5B and 7B) with GRPO and GiGPO baselines.

• Performance Gains:
  • ALFWorld (1.5B, GRPO): Success rate increased from 72.8% to 77.4% (+4.6 points).
  • WebShop (1.5B, GRPO): Success rate increased from 56.8% to 67.0% (+10.2 points).
  • 7B Models: Consistent improvements were observed with both GRPO and the stronger GiGPO baseline (e.g., GiGPO + ENVRL reached 94.5% on ALFWorld).
• Sample Efficiency: ENVRL reached the final performance level of the RL baseline using approximately 68.5% of the total training steps on average, indicating faster convergence.
• Behavioral Improvements: Successful episodes required fewer interaction turns and shorter response lengths, suggesting more precise decision-making and reduced redundant exploration.
• Generalization: ENVRL models trained on standard tasks showed improved robustness on Out-of-Distribution (OOD) test sets with novel room configurations and unseen object types.
• Ablation Studies:
  • Removing either SP or ID degraded performance, confirming the complementary nature of forward and backward dynamics modeling.
  • Removing the decay mechanism led to performance drops, highlighting the necessity of shifting focus from dynamics to rewards over time.
  • Performance scaled positively with the fraction of experience data used for auxiliary training.

5. Significance and Claims

The paper claims that ENVRL offers a general and lightweight approach for LLM agents to learn from environment dynamics. By internalizing the transition mechanisms of the environment through self-supervised objectives, agents can make more effective decisions in long-horizon tasks where outcome rewards are sparse.

The authors position their work as orthogonal to existing methods that focus on fine-grained credit assignment or intermediate reward shaping. Instead, ENVRL treats interaction experience as a rich, independent information source. The framework is presented as a complementary strategy that can be integrated with various policy optimization algorithms (like GRPO or GiGPO) to enhance sample efficiency and generalization without significant computational cost. The work aims to provide a new perspective on agentic RL mechanics, contributing to the development of more robust and autonomous agents.