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Post-training can reduce an agent’s solution coverage despite improving pass@1

huggingface.co by Changdae Oh et al. Editor score 8/10

Across 14 base and post-trained model pairs, the study finds that post-training can improve sampling efficiency while narrowing solution coverage under larger test-time budgets. Its Sharpening Tax measures that trade-off for agentic tasks, informing model and inference-budget choices.

What

The study finds that post-training can improve an agent’s pass@1 while reducing solution coverage under test-time sampling.

Method

Post-training pushes tasks toward two extremes—always solved or never solved—improving sampling efficiency and consistency at the cost of coverage.

Why it matters

Sharpening Tax measures lost test-time scalability and, the paper reports, can be estimated from a few rollouts.

Read on huggingface.co → Code · 1 stars

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