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title: SFT Memorizes, RL Generalizes | daily.dev
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# SFT Memorizes, RL Generalizes

**[Build With GenAI](https://daily.dev/sources/buildwithgenai)** · [@javierblanco](https://daily.dev/javierblanco) · 0 upvotes · 0 comments

## Summary

Research comparing Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for post-training foundation models reveals that SFT causes models to memorize training data and fail on out-of-distribution examples, while RL enables genuine generalization. Using Llama-3.2-Vision-11B on arithmetic and navigation tasks, experiments show SFT performance collapses from 80.8% to 1.3% on new instructions, while RL improves from 80.8% to 91.8%. RL also enhances visual perception in multimodal models, whereas increased SFT compute degrades it. However, SFT remains necessary as a "format teacher" to prepare models for RL training, and RL requires verifiable objectives, making it unsuitable for subjective tasks.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aiwithmike.substack.com/p/sft-memorizes-rl-generalizes>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#computer-vision](https://daily.dev/tags/computer-vision), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

[View this post on daily.dev](https://daily.dev/posts/sft-memorizes-rl-generalizes-3vju22atp)

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