A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning (2510.01132v1)

Abstract: We study what actually works and what doesn't for training LLMs as agents via multi-turn reinforcement learning. Despite rapid progress, existing frameworks and definitions are fragmented, and there is no systematic formulation or analysis of which design choices matter across tasks. We address this gap by first breaking down the design space into three inter-related pillars -- environment, reward, and policy -- and empirically derive a recipe for training LLM agents in situated textual domains. In particular, we test TextWorld and ALFWorld, popular domains for testing situated embodied reasoning, as well as SWE-Gym for more software engineering style tasks. (i) For the environment, we analyze the impacts of task complexity in terms of sizes of the state and action spaces as well as optimal solution length, finding that even simple environments within a domain can provide signal on how well an agent can generalize to more complex tasks. (ii) For the reward, we ablate relative reward sparsity, observing that while dense turn-level rewards accelerate training, performance and stability is highly dependent on the choice of RL algorithm. (iii) And for the agent's policy, we explore the interplay between reward sparsity and biased (PPO, GRPO) and unbiased (RLOO) policy gradient methods in addition to showing how to find the optimal Supervised Fine-tuning (SFT) to RL training ratio given a fixed budget. We distill these findings into a training recipe that guides co-design across the three pillars, facilitating research and practical efforts in multi-turn agentic RL. Code: https://github.com/pearls-lab/meow-tea-taro

• The paper's main contribution is presenting a systematic framework that decomposes multi-turn RL into environment, reward, and policy components.
• It demonstrates that dense reward signals, balanced SFT and RL data, and strong imitation priors significantly boost training efficiency and generalization.
• Experiments in TextWorld, ALFWorld, and SWE-Gym reveal that training in simpler settings can transfer skills to boost performance in complex, multi-turn tasks.

Multi-turn Agentic Reinforcement Learning

Overview

The paper "A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning" presents an empirical framework for training LLMs as agents in multi-turn reinforcement learning (RL) environments. This guide systematically explores the design space for multi-turn RL by decomposing it into three key pillars: environment, reward, and policy. The paper aims to identify effective design choices for creating efficient LLM agents capable of generalization across complex textual and embodied tasks.

Environment

Complexity and Generalization

The environment's complexity fundamentally determines agent performance by introducing challenges like spatial navigation, object manipulation, and extended planning required for multi-turn tasks. Experiments with TextWorld illustrate that agent performance degrades significantly with increased spatial and object complexity (Table 1). Interestingly, while agents struggle more with object complexity, they develop transferable skills that generalize across complex environments.

Figure 1: TextWorld w2-o3-q4 task example. The text in gray are the prompts. The bold text is the objective. The text in blue are the observations and the text in orange are the actions.

Agents trained on simpler environments demonstrated promising generalization to those with increased complexity, highlighting the effectiveness of leveraging transferable skills like spatial exploration and object manipulation.

Policy

Model Priors and Algorithms

The paper emphasizes the significance of model priors, finding that good imitation priors can substantially reduce RL sample requirements. An optimal balance between supervised fine-tuning (SFT) and RL data allocation improves both task-specific accuracy and generalization (Table 2). The analysis shows that biased algorithms (PPO and GRPO) outperform unbiased alternatives (RLOO) in multi-turn settings, with the performance gap increasing in complex environments.

Reward

Dense reward signals significantly enhance multi-turn RL performance, as evidenced by improved training efficiency and convergence rates. The findings indicate an optimal reward density that depends on the optimization algorithm used. For example, PPO benefits most from dense feedback, whereas RLOO remains robust across different reward densities. This suggests that effective reward tuning is crucial to maximize learning efficiency in multi-turn environments.

Performance and Scaling

Empirical results demonstrate that multi-turn RL formulations enable agents to generalize effectively across different domains, including TextWorld, ALFWorld, and SWE-Gym (Figure 2 and 5). By leveraging these specialized environments, the paper establishes guidelines for training autonomous agents capable of handling real-world interactions.

Figure 2: ALFWorld heat {additional_guidance} place task example. The text in gray are the prompts. The bold text is the objective. The text in blue are the observations and the text in orange are the actions.

Figure 3: SWE-Gym getmoto task example. The text in gray are the prompts. The bold text is the objective. The text in blue are the observations and the text in orange are the actions.

Conclusion

The research provides a well-defined, empirically-backed recipe for multi-turn agentic RL that integrates environment, policy, and reward design choices. The paper underlines that multi-turn RL represents a distinct paradigm from single-turn optimization, requiring fundamental rethinking. The practical guidelines developed in this paper pave the way for designing robust agentic AI systems capable of complex multi-turn tasks in various interactive environments. By releasing the accompanying framework, future research can be accelerated, promoting the development of autonomous systems which are better suited to handle real-world dynamics.