Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

2025-02-24 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The paper introduces SR2AM (Self-Regulated Simulative Reasoning Agentic LLM), a novel architecture for efficient agentic reasoning. It decomposes decision-making into three interacting systems: reactive execution (System I), simulative reasoning (System II) using an LLM as a world model, and self-regulation (System III) via a learned configurator. This approach addresses the inefficiency of unconstrained chain-of-thought reasoning in existing agentic LLMs. SR2AM is instantiated in two versions, v0.1-8B and v1.0-30B, trained with supervised learning followed by reinforcement learning. Evaluations across mathematical reasoning, scientific problem-solving, tabular data analysis, and web information seeking show SR2AM-v0.1-8B and SR2AM-v1.0-30B achieve Pass@1 scores competitive with much larger systems (120–355B and 685B–1T parameters, respectively). Notably, SR2AM-v1.0-30B consumes 25.8–95.3% fewer reasoning tokens than comparable agentic LLMs. Analysis indicates RL primarily increases planning horizon by 22.8% rather than planning frequency.

Key takeaway

For AI Scientists and Machine Learning Engineers developing agentic LLMs, you should consider adopting a decomposed architecture with explicit self-regulation and simulative planning. This approach, exemplified by SR2AM, significantly improves efficiency by reducing token consumption (25.8–95.3% fewer tokens) while maintaining competitive performance against much larger models. Integrating a learned configurator allows your agents to dynamically adjust planning depth, leading to more robust and resource-aware long-horizon behaviors.

Key insights

Efficient agentic reasoning emerges from decomposing decision-making into reactive execution, simulative planning, and self-regulation.

Principles

• Decompose agentic reasoning into three distinct, interacting systems.
• Ground deliberation in future-state prediction using a world model.
• Self-regulate planning depth and frequency based on context.

Method

SR2AM trains an LLM to act as a world model, configurator, and actor. It uses supervised learning to encode self-regulated simulative reasoning, then refines with reinforcement learning.

In practice

• Apply to math, science, tabular data, and web information seeking.
• Utilize web search, browser, and Python sandbox tools.
• Code and model artifacts are publicly available.

Topics

• Agentic LLMs
• Simulative Reasoning
• Self-Regulation
• Reinforcement Learning
• Token Efficiency
• World Models

Code references

• sailing-lab/sr2am

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.