A derivation-, tensor-, training-, and runtime-level comparison of draft models, retrieval and prompt lookup, self-speculation, dependent draft heads, multi-candidate exact sampling, token-tree construction, and hardware-aware tree optimization.
A derivation-, state-, scheduler-, and operations-level treatment of continuous-batch speculative decoding, adaptive draft budgets, goodput control, target and draft cache synchronization, evaluation, canary release, and rollback.
A tensor- and runtime-level study of EAGLE feature autoregression, shifted-token conditioning, draft training, dynamic trees, HASS context alignment, and the EAGLE-3 design transition.
A formula-, tensor-, code-, and runtime-level study of DeepSeek-V3 multi-token prediction modules, shared embeddings and heads, teacher forcing, released weight structure, KV ownership, and speculative serving.
A distribution- and serving-state-oriented study note on speculative decoding, draft models, target verification, exact speculative sampling, correction distributions, acceptance rate, speedup ledgers, and KV rollback.
A mechanism-oriented study note on Medusa decoding heads, same-model candidate generation, candidate trees, tree attention masks, typical acceptance, Medusa-1, Medusa-2, and branch KV state.
A mechanism-oriented study note on Lookahead decoding, Jacobi iterations, 2D windows, n-gram pools, lookahead and verification branches, exactness, masks, and KV state.
A derivation- and implementation-oriented study of multi-token prediction objectives, shifted labels, shared-trunk gradients, prediction-head designs, training memory, and the boundary between denser supervision and faster inference.