Test3
2024-03-12
🌌 Introduction
Reinforcement Learning from Human Feedback (RLHF) is often viewed through a technical lens — as a method to fine-tune large language models (LLMs) to align better with user intent. But under the surface lies a deeper, philosophical motivation.
🔍 The Reward Fallacy
The traditional reward signal is inadequate for capturing human values. It’s brittle, hackable, and often misaligned. The idea that we can predefine reward functions for intelligent agents breaks down in the real world.
“A perfectly rewarded system is not necessarily a morally aligned system.”
🤝 RLHF as Dialogical Learning
Human feedback is not just “correction” — it’s conversation. RLHF introduces human intent into the learning loop, which makes the agent a collaborative learner, not a reward-maximizer.
🧠 Conclusion
RLHF isn’t a patch. It’s a philosophical shift:
From solving the reward function → to evolving through feedback.
Perhaps intelligence is not about maximizing — but negotiating.