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Does separating planning from execution improve reasoning accuracy?

Can modular LM architectures that split problem decomposition from solution execution outperform monolithic models? This explores whether decoupling these cognitive operations reduces interference and boosts performance.

Synthesis note · 2026-02-22 · sourced from Reasoning Architectures

When a single monolithic LLM is asked to decompose a problem and solve it, the decomposer doesn't track the solver's capabilities — it generates subproblems without knowing whether the solver can handle them. LM2 addresses this coordination failure by modularizing decomposition, solution, and verification into three separate language models.

The architecture:

The key finding: fine-tuning a separate decomposer LM to coordinate with a larger solver LM outperforms simply prompting a single monolithic LM to decompose and solve. Distilling decomposition abilities from a larger LM to a smaller specialized LM is more generalizable than prompting the monolithic system. The solver is freed to focus on execution; the decomposer is freed to focus on planning.

The generalizability advantage: Monolithic LLM approaches heavily rely on the proprietary LLM being used and fail absolutely when employed with less powerful models. Fine-tuned modular approaches, though cost-effective, maintain generalizability because the decomposition module learns a more abstract planning skill not tied to a specific domain.

The Divide-or-Conquer distillation paper provides direct evidence for this asymmetry: when decomposition and solution abilities are distilled from GPT-4 into smaller models, decomposition ability transfers across domains while solving ability does not. This confirms that planning/decomposition is a more generalizable skill than execution — distilling the ability to break problems down is more portable than distilling the ability to solve specific sub-problems. The decomposer-solver separation isn't just an architectural convenience; it reflects a genuine difference in the transferability of the two cognitive operations.

This is the single-query reasoning instantiation of the same principle that Do hierarchical retrieval architectures outperform flat ones on complex queries? documents at the multi-hop research level. The separation of concerns produces accuracy gains regardless of whether the task is a single complex question or a multi-step research task.

The connection to Can reasoning and tool execution be truly decoupled? is also structural: both ReWOO and LM2 achieve gains by preventing one cognitive operation from contaminating another. ReWOO decouples planning from tool execution; LM2 decouples planning from solution execution.

Planner-Caller-Summarizer decomposition for tool use (from Arxiv/Agents Multi): The "Small LLMs Are Weak Tool Learners" paper extends the decomposer-solver principle to tool-use tasks, demonstrating that modular decomposition into planner, caller, and summarizer enables smaller LLMs to match larger monolithic models. The key insight: each component draws on different LLM facets — planning requires reasoning ability, tool invocation demands accurate request writing, and result summarization requires conclusion-drawing skills. A two-stage training paradigm first finetunes a backbone on the entire dataset for comprehensive understanding, then instantiates and continually finetunes each specialized module on respective sub-tasks. This confirms the generalizability finding: decomposition ability is more transferable than execution ability, and the modular framework facilitates individual component updates — the planner can be upgraded independently of the caller.

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separating decomposer from solver in multi-step reasoning prevents planning-execution interference and improves accuracy