Researchers compared six offline reinforcement-learning methods for distilling reasoning from large language models into smaller ones, finding that SFT, RFT, and RIFT produce nearly identical weight u…
Researchers introduced Adaptive Compatibility Learning with Dynamic Parameter Optimization (ACO), a reinforcement learning system for large language models that autonomously learns relationships betwe…
Researchers introduced VideoKR, a large-scale training corpus of 315,000 video reasoning examples over 145,000 expert-domain videos designed to strengthen knowledge- and reasoning-intensive video unde…
A developer's analysis of LLM fine-tuning versus retrieval-augmented generation (RAG) finds that roughly 70% of production LLM problems are solved by better prompting or RAG, with fine-tuning reserved…
Amazon Web Services announced a new method for improving AI agent tool-calling accuracy using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Amazon SageMaker AI. The approach…
FeynRL, an algorithm-first framework for post-training and fine-tuning large models, has been released as an open-source tool supporting supervised fine-tuning, preference learning, and reinforcement …
MaxText has introduced new post-training capabilities, specifically Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), now available on single-host TPU configurations like v5p-8 and v6e-8. …
Distribution Fine Tuning (DFT) is a new post-training algorithm designed to correct the formulaic and repetitive writing patterns common in standard supervised fine-tuned (SFT) language models. The me…