# J-Zero：从零数据出发的统一挑战者-求解器-裁判协同进化框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-27 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.virxact.com/items/cmtgoiqdy04qirocht22337bn
- 原文链接：https://arxiv.org/abs/2608.26582

## AI 摘要

J-Zero提出统一挑战者—求解器—裁判协同进化框架，支持可验证与不可验证领域的自我改进。挑战者生成更难任务，求解器学习更优回答，裁判则基于已知顺序的偏好对协同适应。J-Zero在可验证领域平均超越基线4.2分，不可验证领域超越8.0分，且至少持续提升十轮，而基线两轮后即退化。

## 正文

Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human supervision. While considerable progress has been made in verifiable domains, self-evolution in unverifiable domains remains substantially less explored. We propose Judge co-adaptation from Zero data (J-Zero), a unified Challenger--Solver--Judge co-evolution framework that supports self-improvement across both domains. The Challenger and Solver co-evolve through an adversarial interaction: the Challenger generates increasingly difficult tasks, while the Solver learns to produce higher-quality responses to them. In parallel, the Judge co-adapts using preference pairs whose ordering is known in advance from how each response was produced, i.e., the Solver's answer over the Challenger's, and its decomposed-and-recombined answer over its one-shot answer, rather than from the Judge's own scores. J-Zero outperforms the baselines by an average of 4.2 points on verifiable and 8.0 points on unverifiable domains, and continues to improve through at least ten iterations, whereas the baselines degrade after two.