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1/ Au revoir, RLVR. New work: EBFT (Energy-Based Fine-Tuning), a post-training method that directly optimizes the long-horizon behavior of model generations, addressing SFT’s deployment-time error amplification without relying on sparse, task-specific rewards.

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Sham Kakade

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266,585 просмотров • 3 месяцев назад •via X (Twitter)

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Our course recommendation of the day is “Post-training of LLMs, ” where you’ll learn how to customize pre-trained language models using Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL). You'll learn when to use each method, how to curate training data, and implement them in code to shape model behavior effectively. Enroll here:
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✨ 🖼️ Generate images with consistent characters without any fine tuning nor training. Consistory is a training-free approach to maintaining subject consistency between text-to-image generations on pretrained models. #NVIDIAResearch 🎨 Test it here:

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