GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
cs.LG, cs.AI
Submitted: 2026-05-25
Updated: 2026-10-10
Code: https://github.com/anonymous/GAC
Terminology
Sources
- Program Synthesis with Large Language Models
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
- Evaluating Large Language Models Trained on Code
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Proximal Policy Optimization Algorithms
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
- SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
- On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
- SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
- Learning to Reason under Off-Policy Guidance
- Towards a Unified View of Large Language Model Post-Training
- Trust-Region Adaptive Policy Optimization
- Anchored Supervised Fine-Tuning
- On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
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