4 papers
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov +6
Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuous prompts. These methods ar…
Monarch-Muon: Structure-Aware Muon for Fast and Memory-Efficient Pretraining
Alexander Miasnikov, Ruslan Kabirov, Gleb Molodtsov +7
Training large language models is constrained by both wall-clock time and memory footprint, and recent matrix-aware optimizers sharpen this tradeoff: Muon can improve optimization…
Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression
Artur Zagitov, Alexander Miasnikov, Maxim Krutikov +5
Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged as a promising direction, off…
Hierarchical Mixture-of-Experts with Two-Stage Optimization
Gleb Molodtsov, Alexander Miasnikov, Aleksandr Beznosikov
Sparse Mixture-of-Experts (MoE) models scale capacity by routing each token to a small subset of experts. However, their routers exhibit a fundamental trade-off: strong load balanc…