In the news
Finetuning with Sampling: SFT Learns Better Than You Think
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers developed an MCMC sampling algorithm that transforms off-policy training data to be more on-policy, enabling supervised finetuning to match reinforcement learning performance.
- Why it matters
- Relevant for engineers building posttraining pipelines who want better generalization and less catastrophic forgetting when adding new capabilities to language models.
- Watch out
- Paper is recent arXiv submission without peer review or independent verification. Practical scalability and computational cost of the MCMC sampling step remain unclear.
- rag
- reinforcement learning
- phi
The patterns behind this
- Structured Reflection (Think Tool)
- Reinforcement Learning from Human Feedback
- Memory Decay & Forgetting Policies
Each one covers how the technique works, when it earns its cost, and where it breaks.
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