Discussion: LLM Distillation and Sparse Architectures
TheZachMueller · x · 2026-07-17
This thread discusses how distillation should actually be done. The author clarifies they mean distilling from a larger to a smaller model within the same tokenizer/model family, not distilling from an API's output tokens.
The quoted content expands on architectural and training strategy insights:
- Notes that certain approaches can achieve a DSV3-like architecture, but require larger parameter scales, sliding windows, and rollout training
- Recommends training combos like Muon, MuP, and GB300
- Suggests Meta would have been better off building Llama4 and subsequent versions this way from the start
- The author ultimately wants to see larger 3T+ models, stronger expert sparsity, KV cache compression, base model releases, frontier RL refinement, and a dedicated distillation paper
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