Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
cs.CL
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- Program Synthesis with Large Language Models
- Evaluating Large Language Models Trained on Code
- Self-training Avoids Using Spurious Features Under Domain Shift
- SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
- Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting
- Measuring Mathematical Problem Solving With the MATH Dataset
- RL Fine-Tuning Heals OOD Forgetting in SFT
- Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models
- ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection
- Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
- Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
- Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
- Magicoder: Empowering Code Generation with OSS-Instruct
- On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
- Qwen3 Technical Report
- AdaMerging: Adaptive Model Merging for Multi-Task Learning
- MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation
- GLM-5: from Vibe Coding to Agentic Engineering
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