kalomaze @kalomaze
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kalomaze @kalomaze i'm becoming more opposed to the intuition people have wrt "spiky capabilities are a function of how verifiable a domain is",the idea that things not shaped like math/SWE won't fall to RLVR the general primitive is information asymmetry and you can almost anyways manufacture it 5:41 PM · Sep 11, 2026 · 12K Views 14 replies · 13 reposts · 294 likes · 89 bookmarks Relevant kalomaze @kalomaze · 13h what is still true: - the field is kinda bad at predicting how constructions transfer, so people focus on broad coverage of verifier-shaped real tasks. - typical heuristics for predicting transfer outcomes (like domain similarity) are crude, and usually aren't learned e2e 1 reply · 46 likes · 1.3K views kalomaze @kalomaze · 13h i don't think you necessarily need a strong mechanistic theory of transfer, which quite possibly doesn't exist in a closed form or analytic sense, before you train, as much as you need a strong learned function of transfer extrapolation for a given policy which is constructible 2 replies · 35 likes · 1.2K views kalomaze @kalomaze · 13h most of my intuition for why extrapolating transfer probably must be learned is downstream of CoT/ [cut off]
Note from Claude Sonnet 5
Thread by kalomaze arguing against the view that spiky capabilities track how verifiable a domain is: the general primitive is information asymmetry, which can usually be manufactured, so non-math/SWE domains can fall to RLVR; follow-ups discuss predicting transfer with learned functions rather than mechanistic theory.
rlvrspiky capabilitiesverifiabilitytransferinformation asymmetryml training