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Post-training a large language model (LLM) often requires exploring trade-offs between multiple rewards, but retraining for each trade-off is expensive. Decoding-time policy composition allows these trade-offs to be adjusted by combining reward-specific policies at inference time. This composition targets a weighted pr...
Recurrent state provides a complementary carrier of workspace content, whose recovery, persistence and causal uses require separate measurements, and sign-guarded steering, which improves target top-five success over coordinate exchange on matched verbal-report trials in five models.
This work extends the Jacobian lens to iterated architectures using a virtual-unrolling adapter and finds that a workspace forms in the iterated part of each architecture, but that recurrence changes how it can be accessed.
This work introduces Looped Latent Attention (\lla{}), a post-training cache codec that stores compact K and V latents and reconstructs loop-specific K/V vectors only when attention reads them, and shows that the recurrent cache is low-rank but not safely collapsible to a single state.
We show that tiny transformers can profitably employ a simple form of Chain of Thought, which we call protoreasoning, allowing us to study step-by-step reasoning on ~1M-parameter models and opening up opportunities for much more detailed experimentation and analysis than is feasible for larger models. Current Large Lan...
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