G-Zero: Self-Play for Open-Ended Generation from Zero Data
📰 ArXiv cs.AI
arXiv:2605.09959v1 Announce Type: cross Abstract: Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcome this, we introduce G-Zero, a verifier-free, co-evolutionary framework for autonomous self-improvement. Our core innovation is Hint-$\delta$, an intrinsic reward that quantifies the predictive shift between a Generator model's unassisted response and its response condi
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Title: G-Zero: Self-Play for Open-Ended Generation from Zero Data
Abstract:
arXiv:2605.09959v1 Announce Type: cross Abstract: Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcome this, we introduce G-Zero, a verifier-free, co-evolutionary framework for autonomous self-improvement. Our core innovation is Hint-$\delta$, an intrinsic reward that quantifies the predictive shift between a Generator model's unassisted response and its response condi
Abstract:
arXiv:2605.09959v1 Announce Type: cross Abstract: Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcome this, we introduce G-Zero, a verifier-free, co-evolutionary framework for autonomous self-improvement. Our core innovation is Hint-$\delta$, an intrinsic reward that quantifies the predictive shift between a Generator model's unassisted response and its response condi
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