TL;DR
A practical framework for locating the binding constraint in an RL system across environments, rollout generation, reward quality, optimization, and evaluation.
Why it matters
Teams often respond to weak results by adding compute. A bottleneck map forces a more useful question: which part of the learning loop is currently preventing additional resources from becoming capability?
Key findings
- 01
Scaling failures usually originate in the interaction between system layers, not a single optimizer setting.
- 02
Environment coverage, reward validity, and evaluation quality should be checked before increasing optimization pressure.
- 03
The right next experiment is the one that distinguishes competing bottleneck hypotheses.
Scaling dimensions
Topics