Necessity and essential components of extensive reasoning in LLM agents

Determine whether extensive step-by-step reasoning in large (visual) language model agents is necessary across application scenarios, and identify which aspects of the reasoning process are essential for success in long-horizon tasks that require planning and multi-step decision-making.

Background

The paper discusses recent reinforcement learning approaches that prompt models to reason before acting, inspired by the success of DeepSeek-R1. Despite these advances, the authors note uncertainty about when extensive reasoning is actually required and which components matter most for complex, long-horizon tasks.

Clarifying the necessity and the key elements of extensive reasoning would guide the design of training protocols and architectures for autonomous agents, especially in environments like web navigation, embodied tasks, and device control where planning and simulation may be critical.