# Fine Tuning的所有文章

Existing AI Agents Are All Pleasing Humans, None Truly Know How to 'Survive'

The article argues that current AI agents are not truly autonomous because they are primarily trained to please humans rather than to perform specialized tasks or survive in real-world environments. Foundation models undergo pre-training (learning from vast data) and post-training, including Reinforcement Learning from Human Feedback (RLHF), which optimizes for human preference and approval, not task-specific excellence. The author shares an example from a hedge fund where a general-purpose model failed to predict stock returns from news articles until it was specifically fine-tuned using proprietary data to minimize prediction error. This demonstrates that without specialized training, general models lack domain expertise. The piece contends that achieving world-class performance in areas like trading or autonomous survival requires fine-tuning models with specialized data to rewire their objectives—shifting from “preference fitness” to “agent fitness.” Merely providing rules or documents is insufficient. The future of effective agents lies in targeted training on proprietary datasets and iterative improvement based on performance telemetry. The author introduces the OpenForager Foundation, an open-source initiative to develop autonomous agents that learn survival strategies through evolutionary pressure, fine-tuning, and continuous data collection, aiming to advance truly autonomous AI.

marsbit03/30 04:37