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This work introduces StreamTTT, which writes long-range history into online-updated fast weights outside the attention context, leaving a short sliding key-value cache dedicated to recent evidence, mitigating attention dilution.
Under the standard approximation of taking gradients at the chunk-start weights, a closed-form state transition is derived that exactly reproduces the chunk-end fast-weight and momentum states of the per-token recurrence of Test-Time Training.
A coherent map of the rapidly expanding landscape of visual RL is provided to provide researchers and practitioners with a coherent map of the rapidly expanding landscape of visual RL and to highlight promising directions for future inquiry.
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