PAY-TO-LEARN: MULTI-AGENT FINE TUNING WITH MICROTRANSACTIONS

Summary from the TLDR Crypto newsletter

This onchain framework allows “student” language models to automatically pay specialist evaluator agents in sub-cent USDC micropayments on Base for structured feedback, eliminating the $2–5 per-annotator bottleneck of RLHF. By choosing evaluators from openly published agent cards, models create a live marketplace that rewards high-quality, domain-specific judgment and maintains feedback relevance as capabilities improve, addressing reward-model drift and echo-chamber issues noted in prior multi-judge research. Early results show orders-of-magnitude cost savings and iterative performance gains, with economic selection preserving evaluator diversity and driving continual improvement. The system is already integrated into Nous Research's Atropos RL framework.