Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Abstract

Language model post-training is applied to refine behaviors and unlock new skills across a wide range of language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most im- portant pieces of the puzzle and the portion with the least transparency. To bridge this gap, we introduce TÜLU 3, a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques. TÜLU 3, which builds on Llama 3.1 base models at 8B, 70B and 405B parameters, achieves results surpassing the instruct versions of Llama 3.1, Qwen 2.5, and Mistral at comparable model sizes. The 405B TÜLU 3 performs compet- itively against closed models such as GPT-4o-mini and Claude 3.5-Haiku or large open models like DeepSeek V3. The training algorithms for our mod- els include supervised finetuning (SFT), Direct Preference Optimization (DPO), and a novel method we call Reinforcement Learning with Verifiable Rewards (RLVR). We detail varying the objective, model initialization, gen- eralization, and over-optimization of this new RL finetuning method. With TÜLU 3, we build a multi-task evaluation scheme for post-training with development and unseen evaluations, standard benchmark implementa- tions, and substantial decontamination of existing open datasets on said benchmarks. The TÜLU 3 release includes model weights, a demo, and the complete recipe — datasets for diverse core skills, a robust toolkit for data curation and evaluation, the training code and infrastructure, and, most importantly, a detailed recipe for reproducing and further adapting the TÜLU 3 approach to more domains.