Researcher Warns Aggressive Exploration in RLVR Can Degrade LLMs
micahgoldblum · x · 2026-10-13
Researcher Micah Goldblum argues in a thread that RLVR for LLMs differs fundamentally from old-school RL: the model is already pretrained and the reward only supervises a tiny fraction of its knowledge, so it could forget most of Wikipedia and still score high reward.
He adds that aggressive exploration can degrade a model, since exploration algorithms lead it toward all kinds of solutions—often bad ones, occasionally gold—and because the reward covers only a small slice of knowledge, recovering from that degradation is costly.
Related event: NYU's ExpDis decouples exploration from optimization in RLVR, beating DAPO(6 posts)→
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