RL Without Verifiable Rewards: Manufacturing Training Signals for Agents
willccbb · x · 2026-08-14
A researcher at Prime Intellect discusses how to manufacture reinforcement learning training signals for AI agents on open-ended tasks lacking ground-truth answers (e.g., writing reports, booking flights).
Key techniques include:
- Environment as Eval: The same environment object serves as the evaluator, generates synthetic data for SFT, and supports on-policy distillation.
- Grounding manufactures signal: By comparing a model's performance with and without source material, the capability gap is used as a learning signal.
- Production traces: Real-world production logs act as the source material to handle unknown task distributions.
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