# Stanford、NVIDIA与UC Berkeley提出无需训练的连续校准验证器

- 来源：elvis (@omarsar0)
- 发布时间：2026-07-08 02:10
- AIHOT 分数：42
- AIHOT 链接：https://aihot.virxact.com/items/cmrazudg802ilihog637h79qu
- 原文链接：https://x.com/omarsar0/status/2074556579580711050

## AI 摘要

Stanford、NVIDIA和UC Berkeley构建了无需训练的验证器，直接从评分token的logits读取连续校准分数，取代离散等级。通过评分粒度、重复评估与标准分解三个旋钮在不微调下提升准确性。在Terminal-Bench V2达86.5%，SWE-Bench Verified 78.2%，RoboRewardBench 87.4%，MedAgentBench 73.3%。该连续分数还可作为密集奖励用于SAC和GRPO，并集成到Claude Code扩展作为任务进度信号。论文：arxiv.org/abs/2607.05391。

## 正文

NEW AI paper worth bookmarking.

This is something I called early， and this paper confirms it： verification has emerged as a new important scaling axis.

Here is the simple explainer and what this paper shows.

We have seen lots of progress in scaling pre-training， post-training， and test-time compute. For post-training and test-time compute， we are still in its early phases. But one of the most important new directions is using LLMs as verifiers. Verifiers are fundamental to scaling AI.

This work from Stanford， NVIDIA， and UC Berkeley builds a training-free verifier that reads a continuous， calibrated score straight off the scoring-token logits instead of trusting a discrete grade.

Three knobs move accuracy without any fine-tuning. Score granularity for cleaner separation， repeated evaluation for lower variance， and criteria decomposition for lower complexity.

The numbers land across very different domains. 86.5% on Terminal-Bench V2， 78.2% on SWE-Bench Verified， 87.4% on RoboRewardBench， and 73.3% on MedAgentBench.

The same continuous score doubles as dense reward for SAC and GRPO and as a task-progress signal shipped in a Claude Code extension.

Paper： https://arxiv.org/abs/2607.05391

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