The chess testbed is meant to probe basic learning and scaling questions in academia
Pavel_Izmailov · x · 2026-07-21
The authors argue that simplified academic testbeds can help answer fundamental questions about learning, scaling laws, data, and training methods.
- They hope the chess setup will be one example of a broader family of such simplified environments.
- They also report validation on natural-language math reasoning with OLMo models.
- In that setting, PT loss remains highly predictive of RL performance, and the slope improves with more PT tokens.
Related event: New Research Proposes Joint Scaling Law for Pretraining and RL(18 posts)→
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