Unverified50% confidenceFactExact time
The design philosophy of the Together post-training platform is to let the user's code fully define the training loop, including data loading and preprocessing, reward function design, loss function computation, optimizer selection, and switching between LoRA fine-tuning and full-parameter fine-tuning.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/24/2026
First Seen
Valid until: 1/22/2027
Sources
Together Post-Training Platform: Keep Control of the Training Loop, Let the Platform Handle the Infrastructure
twittertogethercompute10/22/2026
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UnverifiedThe Together post-training platform provides Recipes as runnable post-training templates covering common training paradigms, on which users can replace data, reward logic, and hyperparameters.80% similarUnverifiedTogether's post-training service supports developer customization of training loop components such as data loading, loss functions, and reward computation, rather than using fixed fine-tuning templates.79% similarUnverifiedTraining frameworks are responsible for adjusting parameters through backpropagation, while inference frameworks focus on efficiently executing trained models, optimizing for low latency, low memory usage, and cross-platform deployment.73% similarUnverified训练秘诀（Training Recipe）包括数据配比策略、学习率调度曲线、过滤规则、对齐技术等，往往比模型架构本身更难被复现73% similarUnverifiedIn the Together Post-Training service, the training methods support supervised fine-tuning and reinforcement learning, and the custom models are owned by the customer.72% similar
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