4 papers · 1 filter
Combating Catastrophic Forgetting in Continual Domain Adaptation via Knowledge Recasting
Zijie Liu, Zhen Tan, Charles Fleming +1
Adapting language models to new domains during post-training risks degrading previously acquired capabilities, a phenomenon commonly known as catastrophic forgetting. Existing miti…
TMS: Trajectory-Mixed Supervision for Reward-Free, On-Policy SFT
Rana Muhammad Shahroz Khan, Zijie Liu, Zhen Tan +2
Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) are the two dominant paradigms for enhancing Large Language Model (LLM) performance on downstream tasks. While RL gener…
HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
Jiangweizhi Peng, Yuanxin Liu, Ruida Zhou +4
Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execut…
Command-V: Pasting LLM Behaviors via Activation Profiles
Barry Wang, Avi Schwarzschild, Alexander Robey +4
Retrofitting large language models (LLMs) with new behaviors typically requires full finetuning or distillation-costly steps that must be repeated for every architecture. In this w…