RL-for-LLMs is Scaling Up in Production
finbarrtimbers · x · 2026-07-15
The author argues that alt neolabs like Oak will be at a disadvantage if they ignore the current cutting-edge RL-for-LLMs trajectory.
He emphasizes that RL is already being deployed at scale; if teams do not genuinely understand the current problems and boundaries of RL, they will slow down their own iteration speed and struggle to keep up with mainstream progress.
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