5 papers · 1 filter
Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation
Kumari Nishu, Han-Byul Kim, Santosh Chilkunda +13
Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challenging. SD improves the inferen…
TIDE: Every Layer Knows the Token Beneath the Context
Ajay Jaiswal, Lauren Hannah, Han-Byul Kim +3
We revisit a universally accepted but under-examined design choice in every modern LLM: a token index is looked up once at the input embedding layer and then permanently discarded.…
Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why
Mohammadreza Armandpour, Fatih Ilhan, David Harrison +6
On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under whi…
SpecMD: A Comprehensive Study On Speculative Expert Prefetching
Duc Hoang, Ajay Jaiswal, Mohammad Samragh +1
Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model's parameters is used during each inference. However, to translate this spar…
MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
Ajay Jaiswal, Lauren Hannah, Han-Byul Kim +4
Understanding how transformer components operate in LLMs is important, as it is at the core of recent technological advances in artificial intelligence. In this work, we revisit th…