aiqbench analysis
ReTreVal introduces a training-free framework that enables large language models to retain failure context and learn strategies across problems without fine-tuning, significantly improving performance on MATH-500 and MMLU-Pro. By utilizing adaptive tree exploration and self-rewriting memory, the method allows smaller 32B models to compete with larger single-pass systems while confirming genuine error recovery. This approach represents a notable shift toward efficient inference-time learning that eliminates the need for gradient updates to enhance reasoning capabilities.
ReTreVal: Reasoning Tree with Validation and Cross-Problem Memory for Large Language Models
arXiv:2601.02880v3 Announce Type: replace-cross Abstract: Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no wiser than it was on problem 1. We present ReTreVal (Reasoning Tree with Validation), a training-free framework that closes this gap through adaptive tree exploration with tool-augmented node refinement, typed-failure backtracking that injects categorized error context into the recovered branch, and a self-rewriting memory that accumulates and revises strategy entries across problems, enabling inference-time cross-problem learning on any fixed, unmodified LLM without fine-tuning. ReTreVal achieves 85.8% pass@1 on MATH-500 (+8.6 pp over Zero-Shot CoT, +8.6 pp over the strongest baseline Self-Refine) and 54.4% on MMLU-Pro (+15.3 pp over Self-Refine), with a 3.4:1 win-to-regression ratio confirming genuine error recovery rather than noise. These capabilities, previously requiring gradient updates, allow a 32B model to compete with much larger single-pass systems.
Abstract:Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no wiser than it was on problem 1. We present ReTreVal (Reasoning Tree with Validation), a training-free framework that closes this gap through adaptive tree exploration with tool-augmented node refinement, typed-failure backtracking that injects categorized error context into the recovered branch, and a self-rewriting memory that accumulates and revises strategy entries across problems, enabling inference-time cross-problem learning on any fixed, unmodified LLM without fine-tuning. ReTreVal achieves 85.8% pass@1 on MATH-500 (+8.6 pp over Zero-Shot CoT, +8.6 pp over the strongest baseline Self-Refine) and 54.4% on MMLU-Pro (+15.3 pp over Self-Refine), with a 3.4:1 win-to-regression ratio confirming genuine error recovery rather than noise. These capabilities, previously requiring gradient updates, allow a 32B model to compete with much larger single-pass systems.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2601.02880 [cs.AI]
(or arXiv:2601.02880v3 [cs.AI] for this version)
Full text under the publisher's open licence · 152 words
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