Soft-MTP drops a noisy residual in multi-token prediction training
From 167M to 1.2B parameters, Soft-MTP lowers next-token perplexity and speeds speculative decoding without new parameters.
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From 167M to 1.2B parameters, Soft-MTP lowers next-token perplexity and speeds speculative decoding without new parameters.
On Llama and Qwen pairings, it was up to 4.1x faster than standard decoding and beat a fixed window in 8 of 10 settings.
Dynamic Depth Sampling draws training depth with decaying probabilities, at half the overhead of training every depth.
vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.
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