Entropy Collapse as Spent Potential Energy: Rethinking Exploration Loss in RL Training
tensorqt · x · 2026-10-07
@tensorqt offers a conceptual framing of entropy collapse: he doubts there's a clearly sample-efficient fix, preferring to see it as spending the model's 'potential energy' — as the model learns to sample across states during RL, it loses exploration capability and potential energy must be reinserted to explore again, though not necessarily via SFT.
A conceptual framework for thinking about exploration loss rather than a concrete algorithm.
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