Process-Oriented Learning

Process-Oriented Learning trains AI systems to follow good reasoning processes rather than just produce correct final answers. The focus is on howthe AI reaches conclusions, not just what conclusions it reaches.

Process vs Outcome Supervision

Traditional training often uses outcome supervision: reward correct answers, penalize wrong ones. Process supervision instead evaluates each step:

• Outcome: "Is the final answer correct?"
• Process: "Is each reasoning step valid?"

Why Process Matters

An AI might reach correct answers through:

• Sound reasoning (good)
• Memorization (brittle)
• Lucky guessing (unreliable)
• Exploiting spurious correlations (dangerous)

Process supervision helps ensure the AI is actually reasoning correctly, making its behavior more predictable and trustworthy.

Applications

• Math: Verify each step of a proof
• Coding: Check reasoning about code logic
• Analysis: Evaluate argument structure
• Planning: Assess each decision in a sequence

Connection to Alignment

Process-oriented learning supports alignment by:

• Making AI reasoning more transparent
• Catching reward hacking earlier
• Enabling better interpretability
• Reducing reliance on potentially gamed outcomes

Challenges

• Defining "good process" is often harder than checking outcomes
• Requires more detailed human feedback
• May slow down training
• Some tasks don't have clear process steps

See Also

• RLHF
• Interpretability
• Reward Hacking
• Recursive Reward Modeling