Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents (2506.00320v1)

Abstract: Recent progress in reasoning with LLMs, such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horizon AI agents tasks. In this work, we propose Dyna-Think, a thinking framework that integrates planning with an internal world model with reasoning and acting to enhance AI agent performance. To enable Dyna-Think, we propose Dyna-Think Imitation Learning (DIT) and Dyna-Think Dyna Training (DDT). To initialize a policy with Dyna-Think, DIT reconstructs the thinking process of R1 to focus on performing world model simulation relevant to the proposed (and planned) action, and trains the policy using this reconstructed data. To enhance Dyna-Think, DDT uses a two-stage training process to first improve the agent's world modeling ability via objectives such as state prediction or critique generation, and then improve the agent's action via policy training. We evaluate our methods on OSWorld, and demonstrate that Dyna-Think improves the agent's in-domain and out-of-domain performance, achieving similar best-of-n performance compared to R1 while generating 2x less tokens on average. Our extensive empirical studies reveal that 1) using critique generation for world model training is effective to improve policy performance; and 2) AI agents with better performance correlate with better world modeling abilities. We believe our results suggest a promising research direction to integrate world model simulation into AI agents to enhance their reasoning, planning, and acting capabilities.

Evaluation of Simple-Dyna: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents

The paper "Simple-Dyna: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents" introduces a novel framework aimed at increasing the performance efficiency of AI agents, specifically those reliant on LLMs. The key contribution of this work is the Simple-Dyna framework, which integrates reasoning, acting, and world model simulation into the AI agent's thinking process.

Methodological Approaches and Components

The proposed Simple-Dyna framework combines the following components:

1. Dyna-Think Imitation Learning (DIT): This technique reconstructs the thinking process of an expert LLM, concentrating on concise and action-relevant world model simulations. It utilizes the distilled cognitive patterns from models like DeepSeek-R1 to initialize a policy capable of efficiently handling complex environments while generating fewer tokens.
2. Dyna-Think Dyna Training (DDT): Building upon the traditional Dyna approach, DDT uniquely implements both policy learning and world model training procedures within a single LLM. It leverages a two-stage training paradigm where the agent initially focuses on enhancing world modeling capabilities followed by policy improvements. The framework evaluates different representation methods like next-state prediction, state-difference modeling, and critique generation for effective world model simulation.

Empirical Evaluation and Results

The paper evaluates the efficacy of Simple-Dyna using the OSWorld benchmark, a domain-rich environment necessitating the interaction with various applications and platforms. The results illustrate that the Simple-Dyna models, based on Qwen2.5-32B-Instruct, achieve similar best-of-n performance to the DeepSeek-R1 model, albeit requiring fewer computational resources—specifically generating fewer tokens and having a smaller model size.

Notably, the paper shows impressive empirical results in both in-domain (ID) and out-of-domain (OOD) tasks, indicating substantial scalability and adaptability of the model across different domains. The model's robust performance under varied configurations illustrates the potential of integrated world model simulation in enhancing long-horizon AI tasks.

Theoretical and Practical Implications

This framework underscores the importance of concise and efficient reasoning models in the context of AI agent tasks. By emphasizing world model simulation, Simple-Dyna fosters a paradigm where LLMs don't merely react but instead predict and synthesize potential outcomes and course actions based on internalized models of their environments.

Practically, implementations of Simple-Dyna could lead to more efficient AI agents capable of executing complex workflows across numerous platforms without excessive computational overhead. It marks a shift towards the development of AI agents that are not only reactive but possess a nuanced understanding of environmental dynamics.

Future Directions

The research suggests that further scaling of both world model and policy data, potentially through automated evaluative measures, could enhance model robustness and efficiency. Moreover, additional exploration into automated test-time reasoning frameworks would optimize the agent's ability to handle novel tasks autonomously.

In conclusion, Simple-Dyna establishes a promising direction for AI agent development by synthesizing reasoning, acting, and simulation, contributing significantly to the refinement and scalability of intelligent agents in practical applications.