Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

Researchers introduced SR²AM, a three-part framework for agentic reasoning: reactive execution, simulative reasoning, and a learned self-regulator.

The regulator decides when to plan, how far ahead to simulate, and when to act directly, making reasoning more selective.

Reinforcement learning helps the model extend its planning horizon, improving future-state prediction and decision quality.

In a 30B-scale model, SR²AM reportedly matches or approaches much larger systems while using far fewer reasoning tokens.