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.

References

However, it remains unclear whether extensive reasoning is necessary for all scenarios \citep{shojaee2025illusionthinkingunderstandingstrengths}, and what aspects of such reasoning is essential for long-horizon tasks \citep{yu2025dynathinksynergizingreasoningacting}.

— Dyna-Mind: Learning to Simulate from Experience for Better AI Agents  (2510.09577 - Yu et al., 10 Oct 2025) in Section 2, Related Work (Training (V)LM agents)

Two mechanisms plausibly explain this; distinguishing between them requires future work. Agents may externalise reasoning to files because their context windows are bounded, making documentation a form of working memory rather than a reference; the prominence of plans and thoughts/ directories is consistent with this possibility. Alternatively, they may not validate against prose because a cheaper oracle, the test suite, is invoked directly.

— From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation  (2608.20195 - Gao et al., 20 Aug 2026) in Section 5.3, “Why the difference from human developers matters”