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LiveCodeBench

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RESEARCH · 1 SOURCE · arXiv cs.AI

CoVer: information-gain rewards and diversity-pruned tests for more reliable code generation

arXiv:2609.21208v1 introduces CoVer, a single-policy GRPO framework that co-trains a coder and verifier using an information-gain reward (mutual information with a graded ground-truth correctness signal) combined with a three-stage diversity-aware pruning of self-generated tests. The authors report improvements in one-shot pass@1 over a Qwen2.5-Instruct backbone (+5.8 points at 7B, +7.1 points at 14B) across five benchmarks and a +3.5-point lift when integrated into the CodeT ranking pipeline with a 7B backbone.

7.0

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