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cs.LG2025
Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm
Kaisen Yang, Tinghe Zhang, Rushi Shah +4
Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or split across separate component…
cs.LG2025
Towards a Unified View of Large Language Model Post-Training
Xingtai Lv, Yuxin Zuo, Youbang Sun +8
Two major sources of training data exist for post-training modern language models: online (model-generated rollouts) data, and offline (human or other-model demonstrations) data. T…
cs.LG2025
AMFT: Aligning LLM Reasoners by Meta-Learning the Optimal Imitation-Exploration Balance
Lixuan He, Jie Feng, Yong Li
Large Language Models (LLMs) are typically fine-tuned for reasoning tasks through a two-stage pipeline of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL), a pr…