🔗 Source: arXiv

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

🚀 Technical Novelty

  • Mechanism: Decomposes agentic decision-making into three distinct, learnable stages: a reactive actor (System I), a simulative planner that uses the LLM as an implicit language-space world model (System II), and a learned configurator (System III) that dynamically gates whether to plan, continue planning, or act directly at each turn.
  • Nuance: Unlike unconstrained chain-of-thought or always-on planning paradigms, it explicitly decouples the decision to plan from the planning process itself, using RL to shift behavior toward longer simulation horizons rather than increased planning frequency, thereby eliminating token bloat without sacrificing deliberation depth.

💡 Yield

  • SR2AM-v1.0-30B achieves Pass@1 performance competitive with 685B–1T parameter agentic LLMs while consuming 25.8–95.3% fewer reasoning tokens across math, science, tabular, and web-reasoning benchmarks.
  • RL training increases the average planning horizon by 22.8% while growing planning frequency by only 2.0%, empirically proving that extended forward simulation is more efficient than frequent short deliberation for agentic tasks.

⚠️ Limitations

  • Relies on the LLM itself as an implicit language-space world model rather than a dedicated, trained simulator, which may limit accuracy in highly dynamic, non-textual, or physically grounded environments.
  • The scalable v1.0 data pipeline reconstructs structured plans from pretrained LLM traces via an annotator model, potentially inheriting reasoning biases, hallucinations, or structural errors from the source models.