RL Beyond the Verifiable
The article discusses extending reinforcement learning (RL) to domains where success is not easily verifiable, exploring methods like reward modeling, human feedback, and learned reward functions to train AI systems on tasks that lack clear, objective criteria for correctness.
Background
- Reinforcement Learning from Human Feedback (RLHF) is the standard method for fine-tuning large language models (LLMs) like GPT-4, but it relies on humans rating outputs, which is slow and expensive.
- "Verifiable" tasks (math, coding) have objective right/wrong answers, allowing automated reward signals without human judges.
- The article argues that many important AI training scenarios lack such verifiable ground truth, making current RL approaches harder to apply.
- It explores alternatives like using AI feedback (RLAIF), self-play, and process-based rewards to extend RL to subjective or complex domains outside of math and code.