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A thought experiment explaining why unattended AI agents tend to redo completed work on long multi-step runs — because finished steps stay in context unmarked — and asking what explicit task-tracking approaches people use for overnight agent jobs.
The author discusses a persistent challenge in multi-step AI agents where state contamination from previous steps leads to hallucinated outputs, and notes the need for cleaner context boundaries despite available tools like EnterPro Agent Builder.
The tweet lists 15 LLM fine-tuning techniques and introduces ART (Agent Reinforcement Trainer), an open-source framework from OpenPipe for training multi-step agents using GRPO, with serverless RL support via W&B Training.
The article discusses the challenge of evaluating LLM-based agents that perform multi-step reasoning, noting that scoring only the final output is insufficient because agents may take wrong paths and recover by accident, and raises questions about how to evaluate the trajectory without manual review.