Recursive Reward Modeling is an AI alignment technique that uses AI assistance to help humans evaluate complex AI behavior — when the AI's outputs are too complex for direct human evaluation, an AI assistant helps decompose and evaluate the output, with the human retaining final authority.

Recursive Approach

• Level 0: Human directly evaluates simple AI outputs — standard RLHF.
• Level 1: AI assists human evaluation of more complex outputs — decomposes, summarizes, highlights issues.
• Level 2: AI helps evaluate the AI assistant from Level 1 — recursive trustworthy evaluation.
• Amplification: Each level amplifies human evaluation capability — reaching progressively more complex tasks.

Why It Matters

• Superhuman Tasks: As AI capabilities surpass human evaluation, recursive reward modeling maintains oversight.
• Decomposition: Complex outputs are decomposed into human-evaluable sub-problems — divide and conquer.
• Alignment Scaling: Provides a path to aligning increasingly capable AI systems — human oversight scales with AI capability.

Recursive Reward Modeling is AI-assisted human oversight — using AI to help humans evaluate AI outputs for scalable alignment of superhuman systems.