J-Zero introduces a unified Challenger–Solver–Judge co‑evolution framework that enables self‑improvement of language models without requiring external supervision. The Challenger generates increasingly difficult tasks, the Solver learns to produce better responses, and the Judge adapts using preference pairs derived from the Solver’s own outputs rather than from its own scores. Experiments show J‑Zero outperforms baselines by an average of 4.2 points on verifiable tasks and 8.0 points on unverifiable tasks, maintaining improvement over ten iterations while baselines degrade after two.
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 mad...
J-Zero introduces a unified Challenger–Solver–Judge self‑evolution framework that operates without any initial data. The Challenger and Solver co‑evolve through adversarial task generation and response improvement, while the Judge adapts using known preference pairs derived from the Solver’s outputs rather than its own scores. Experiments show J‑Zero surpasses baselines by 4.2 points on verifiable tasks and 8.0 points on unverifiable tasks, maintaining improvement over ten iterations versus baseline degradation after two.
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthe...
arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.
arXiv:2607. 20083v1 Announce Type: cross Abstract: Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models.
arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.
arXiv:2609.22221v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the dev...
arXiv:2607. 18973v1 Announce Type: cross Abstract: Textual skills provide a lightweight way to improve frozen language-model agents, but their self-evolution normally requires a stable validation signal.
arXiv:2606. 09052v1 Announce Type: cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision.
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
arXiv:2606. 04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability.