Ouroboros: Human-Led Recursive Reinforcement for Autoregressive Language Models

Large Language Models (LLMs) typically rely on Reinforcement Learning from Human

Feedback (RLHF) or direct preference optimization to align generated text with human values.

We introduce Ouroboros, a recursive, human-led reinforcement (HLRR) method in which a

human curator cyclically distills their own evaluative judgments, meta-commentary, and persona

into the model’s future behavior. Unlike conventional RLHF—which treats human feedback as

a static reward signal—Ouroboros closes the loop between model and supervisor: each model

generation is archived, summarized, and syntactically “stretched” into labyrinthine prompts that

probe the model’s reasoning limits; the resulting conversation is then scored and rewritten by the

same human, producing richer signals that simultaneously assess content, self-consistency, and

identity coherence.