open-ended-generation

This paper introduces a unified framework for test-time diverse generation in large language models, categorizing methods by where diversity is injected (surface-level vs. specification-level). It proposes specification-level methods that generate diverse intermediate specifications, achieving better output diversity across five open-ended tasks and four backbone models while maintaining quality.

This paper introduces G-Zero, a verifier-free framework that enables autonomous large language model self-improvement through co-evolutionary training using intrinsic rewards and hint-based guidance. It aims to overcome the limitations of proxy LLM judges in open-ended tasks by deriving supervision from internal distributional dynamics.