mlsanityData to 7 Oct 2026

Characterizing a Configuration Where Inference-Time PRM-Pruned Fragment Grafting Is Inert: Evidence from Three Reasoning LMs

arXiv 2610.00047 ↗First posted 3 Sep 2026cs.AI cs.CLcited by 0

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Abstract (arXiv)

Diversity collapse in parallel chain-of-thought has motivated inference-time interventions built on a natural design: when a process reward model (PRM) prunes a chain, its high-PRM prefix is extracted and grafted verbatim as an in-context demonstration into a still-decoding sibling. We isolate this mechanism, PRM-Pruned Fragment Grafting (PPFG), as the most cost-minimal operationalization of cross-trajectory step-level transfer, and test it at the operating point where prior fragment-grafting work reports gains only under additional compensating ingredients. On Qwen2.5-7B-Instruct with Math-Shepherd on full MATH500 (n=500, three seeds), PPFG in both stagnation- and random-targeting variants is statistically indistinguishable from an independent parallel-CoT baseline on every measured axis. We characterize why: a four-bucket classification of 322 stagnation-rule injection events shows only 14% targeted a genuinely struggling chain; the rest landed on chains that had already succeeded, were near completion, or sat on a flat PRM plateau, states a rescue graft cannot change. No compound-gate refinement jointly achieves well-targeted firing and adequate density, and a random control matches the same parity at 2.4x the firing rate, so the inertness is not heuristic-specific. The finding replicates across three base LMs, six benchmarks, a second PRM, and a compatibility-gate sweep; two-one-sided-tests analysis promotes the parity to positive equivalence on all twelve Qwen/LLaMA cells. A per-event spot-check finds injected chains prune at 2.75x the matched-step rate, but a surviving-sibling counterfactual finds no population-level compensation. A hindsight oracle bounds any per-problem gain from choosing PPFG over independent at +0.13 pp. We contribute an equivalence-testing template for establishing inference-time mechanism nulls, with every claim scoped to its tested operating point.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 3 Sep 2026.

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References · 23

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  7. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  8. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 948
  9. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  10. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 168
  11. 2025VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Datacited by 39
  12. 2025Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviorscited by 28
11 more
  1. 2025GRPO-λ: Credit Assignment improves LLM Reasoningcited by 21
  2. 2026Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scalingcited by 9
  3. 2026RTMC: Step-Level Credit Assignment via Rollout Treescited by 9
  4. 2025Mode-Conditioning Unlocks Superior Test-Time Scalingcited by 8
  5. 2026Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimizationcited by 7
  6. 2025Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scalingcited by 4
  7. 2026Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitchingcited by 3
  8. 2026Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inferencecited by 2
  9. 2026The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solvingcited by 1
  10. 2026All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Modelscited by 1
  11. 2026VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selectioncited by 1

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