RL via Verifiers: A New Opportunity for Agents
shaneguML · x · 2026-07-18
This repost summarizes the core thesis of an ICML invited talk: whoever can turn messy real-world outcomes into reliable, scalable reward signals will train capabilities that foundation models alone cannot achieve.
Key examples provided include:
- Coding agents suddenly became usable partly because code naturally comes with "free verifiers" like compilers and type checkers.
- Teams can build reinforcement learning around these verifiers, and as compute scales, capabilities stack up.
- The author believes the next batch of unicorns will emerge from startups building verifiers for more complex domains.
- Several directions were highlighted: evaluating via physical fits in automated labs, auditing LLM judge rubrics before massive RL, and training open-source models on legal benchmarks.
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