RL Researcher Revives "Precompute and Reuse": Why Keep Starting Over?
furongh · x · 2026-10-23
Researcher furongh recalls the Reincarnating Reinforcement Learning workshop at ICLR 2023 in Kigali, where he built slides at 4 a.m. for a talk on adaptable RL in an ever-changing world.\n\nHis recurring thesis: precompute and reuse — given how much compute has already been spent learning something useful, why keep starting from scratch? He notes this now looks almost too obvious with foundation models.\n\nThe quoted tweet introduces Meta², an approach for recursive self-improvement: it learns evolution guidance offline — how and when to evolve — and applies it dynamically within each run, addressing the problem of agent harnesses being frozen before the run even starts.
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