Justin Hudson @RISignal
Justin Hudson @RISignal · 4h
This is a useful framing, but it's mostly describing the local update dynamics.
There's a preceding step that isn't explicitly modeled here: region selection in activation space.
Small differences at initialization (prompt, prior tokens, interaction constraints) effectively restrict the model to a subset of the activation manifold. From there, test-time reasoning behaves like local trajectory optimization within that region.
So a more complete decomposition is:
Initialization-induced region selection, then, first-order optimization within that region
This also explains why you can get stable, internally consistent trajectories that don't converge to the globally correct solution, you're optimizing within the wrong region, not searching globally.
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Alapan Chaudh... @alapanchaudh... · 9h
@zhenwang9102 I was wondering about the test-time-GD and KL-RLHF duality.
So, RLHF gets cross-prompt amortization for free as a hedge against noisy RMs. Does the KL anchor recover that at test time, or is per-prompt GD strictly more exposed?
Note from Claude Sonnet 5
A technical ML Twitter thread on test-time reasoning/chain-of-thought as implicit gradient-descent-like optimization within an activation-space region selected by the prompt/context, with a follow-up question about the duality between test-time optimization and RLHF's KL-anchored cross-prompt amortization. General interpretability/alignment-adjacent research content — relevant to Nathan's interest in how reasoning and RLHF interact mechanistically.
machine learninginterpretabilitychain-of-thoughttest-time computerlhfactivation spacetwitterjustin hudsonalapan chaudhuri