Researchers Debate RL Generalization Limits: If It Generalized Well, Labs Wouldn't Need to Build Envs
brianryhuang · x · 2026-10-05
Researcher willcb argues that parallelizing research is one of the strongest reasons to use RL, and that RL does yield some generalization. But he contends that if RL generalized out-of-distribution well enough that you could just scale on math or board games to target domain X, the compositional benefits of joint multi-task RL would be strong enough that frontier labs would find a way to avoid MOPD. He also notes many RL recipes (especially async) hit a "max steps" limit before instability, and that in such a world people would aggressively scale batch size — citing Xiaomi MiMo as an extreme example.
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