ReTreVal: Reasoning Tree with Validation and Cross-Problem Memory for Large Language Models

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

ReTreVal, a hybrid framework, significantly enhances Large Language Model (LLM) multi-step reasoning by integrating Tree-of-Thoughts exploration, self-refinement, LLM-based critique scoring, and reflexion memory. It dynamically adjusts tree depth based on problem complexity and employs a dual validation mechanism, combining local and LLM critic scores. A persistent memory buffer stores insights from successful paths and failure patterns, enabling cross-problem learning. Experiments using Qwen 2.5 7B demonstrated ReTreVal outperformed ReAct, Reflexion, and Self-Refine across 500 mathematical problems and 100 creative writing tasks, achieving an average score of 6.92/10 in math (4.4% over ReAct) and 7.88/10 in creative writing (19.4% over ReAct), notably eliminating low-score failures in math.

Key takeaway

For AI Engineers developing LLM-powered agents for complex problem-solving, ReTreVal offers a robust framework to significantly enhance reasoning accuracy and coherence. You should consider integrating adaptive tree exploration, LLM-based critique, and persistent memory into your agent designs. This approach reduces reasoning failures and enables cumulative learning, leading to more reliable and generalizable solutions across diverse domains like mathematics and creative generation.

Key insights

Combining structured exploration, iterative refinement, explicit validation, and persistent memory significantly enhances LLM multi-step reasoning.

Principles

• Component integration enhances LLM performance.
• Explicit validation prevents error propagation in reasoning.
• Persistent memory enables cumulative, cross-problem learning.

Method

ReTreVal constructs an adaptive reasoning tree, refines nodes via self-critique, scores them with an LLM critic, and prunes low-quality paths. A reflexion memory stores insights for cross-problem learning.

In practice

• Implement adaptive tree depth based on problem complexity.
• Employ dual LLM-based critique scoring for validation.
• Maintain a FIFO memory buffer for cross-problem insights.

Topics

• Large Language Models
• Multi-Step Reasoning
• Tree-of-Thoughts
• Self-Refinement
• Reflexion Memory
• LLM Critique
• Qwen 2.5 7B

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.