Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data 事件

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data arXiv:2603.19294v3 Announce Type: replace-cross Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new data is expensive to collect. Moreover, true intelligence goes far beyond verifiable tasks. Therefore, we need