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COMPANY · ENTITY #3693

MATH500

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

Study finds PRM-pruned fragment grafting (PPFG) produces no measurable benefit on tested reasoning LMs

The paper isolates PRM-Pruned Fragment Grafting (PPFG) as a minimal inference-time intervention and tests it on Qwen2.5-7B-Instruct with Math-Shepherd across MATH500 (and replicates across three base LMs, six benchmarks, a second PRM, and gate sweeps). PPFG — in both stagnation- and random-targeting variants — was statistically indistinguishable from an independent parallel-CoT baseline, and analyses attribute inertness to mis-targeted firing (only ~14% of injections hit genuinely struggling chains) and plateau states a graft cannot rescue; the authors also provide an equivalence-testing template for null results.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

OBC-Prune: outcome-based calibration improves pruning for large reasoning models (arXiv:2609.17890v1)

This paper proposes OBC-Prune, an outcome-based calibration method for pruning large reasoning models that builds difficulty-matched correct/incorrect rollouts and uses intervention-based analysis to score the causal importance of reasoning sentences. The per-token importance weights are used to rescale calibration activations for one-shot pruning methods (SparseGPT, Wanda, ALPS), and experiments on DeepSeek-R1-Distill-Qwen 1.5B/7B/14B at 40% and 50% sparsity report consistent improvements over calibration baselines on MATH500, LiveCodeBench, and AIME 2025.

7.0