5 papers · 1 filter
Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
Yifan Niu, Han Xiao, Dongyi Liu +4
On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain experts to student models. Howe…
AVSD: Adaptive-View Self-Distillation by Balancing Consensus and Teacher-Specific Privileged Signals
Duy Nguyen, Hanqi Xiao, Archiki Prasad +7
Self-distillation enables language models to learn on-policy from their own trajectories by using the same model as both student and teacher, with the teacher being conditioned on…
Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
Hao Wang, Guozhi Wang, Han Xiao +8
Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited by sparse rewards and long hori…
Conflict-Resolving and Sharpness-Aware Minimization for Generalized Knowledge Editing with Multiple Updates
Duy Nguyen, Hanqi Xiao, Archiki Prasad +3
Large language models (LLMs) rely on internal knowledge to solve many downstream tasks, making it crucial to keep them up to date. Since full retraining is expensive, prior work ha…
Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
Hanqi Xiao, Yi-Lin Sung, Elias Stengel-Eskin +1
Post-training quantization (PTQ) reduces a model's memory footprint by mapping full precision weights into low bit weights without costly retraining, but can degrade its downstream…