Together Launches Post-Training Service: Customize Your Frontier Open-Source LLM

Open-source LLMs are becoming increasingly capable, but shaping them into a "custom model" that fits your specific business needs remains a challenge for many teams. Together's newly launched Post-Training service targets exactly this pain point: it lets developers post-train open-source models such as Qwen, GLM, and Kimi as flexibly as writing a training loop, while leaving the complexity of underlying infrastructure to the platform.

Why Post-Training Has Become Essential

General-purpose LLMs perform well across a wide range of tasks, but vertical scenarios often require additional domain adaptation. Post-training aligns a base model to specific objectives through supervised fine-tuning (SFT) or reinforcement learning (RL), and has become a key path for enterprises building differentiated AI capabilities.

Together's positioning is clear: you own the model, you control the training logic, and the platform handles the heavy lifting—compute scheduling, distributed training, and fault tolerance. This division of labor lowers the barrier to post-training while retaining sufficient flexibility.

"Post-Training" is defined relative to "Pre-Training." Pre-training refers to training a base model from scratch on massive unlabeled text, which often costs millions of dollars and requires thousands of GPUs; post-training, by contrast, makes secondary adjustments on top of an existing base model using a small amount of domain data, at a cost that is orders of magnitude lower. What the industry often calls "Alignment" also falls under post-training, including instruction tuning and preference optimization (such as RLHF/DPO). For the vast majority of enterprises, pre-training from scratch is neither economical nor necessary—post-training is the realistic path to turning a general model into a "custom model."

Core Capabilities: Dual Paths of SFT and RL

According to the official introduction, the service supports two mainstream post-training paradigms:

• Supervised Fine-Tuning (SFT): Using high-quality input-output paired data, the model learns the response patterns of a specific task. Suitable for scenarios with labeled data, such as customer service scripts and code-generation style transfer.
• Reinforcement Learning (RL): Optimizing model behavior through reward signals, suitable for tasks that are difficult to label directly but whose quality can be evaluated, such as reasoning chain optimization and agent decision-making.

The key point is "you write the training loop"—developers can customize data loading, loss functions, reward computation, and other components, rather than being locked into a fixed fine-tuning template. This design is especially appealing to teams that need fine-grained control over the training process.

Supervised Fine-Tuning (SFT) is the most common post-training method. In essence, it continues gradient descent on top of a pre-trained model using labeled "input-output" samples, so that the model imitates the distribution of the target data. Typical data volumes range from a few hundred to tens of thousands of examples, and the training objective is usually the cross-entropy of next-token prediction.

Reinforcement Learning (RL) is different: it does not directly tell the model "what the correct output is," but defines a reward function to evaluate how good an output is, and the model maximizes the reward through trial and error. Common algorithms include PPO (Proximal Policy Optimization) and GRPO (Group Relative Policy Optimization, which DeepSeek^Ad used to train reasoning capabilities). RL is particularly suitable for tasks where "it is difficult to write a standard answer, but easy to judge good from bad," such as mathematical reasoning, code correctness, and dialogue helpfulness. Compared with SFT, RL training is more unstable, more sensitive to hyperparameters, and more compute-intensive—which is exactly where the value of platform infrastructure becomes apparent.

Infrastructure Handled by Together

Post-training, especially RL, places far higher demands on compute and engineering than inference. Together takes on distributed training orchestration, GPU cluster management, checkpoint saving and recovery, and more. For teams lacking large-scale training experience, this can significantly shorten the time from idea to usable model.

The platform's supported models cover mainstream open-source series such as Qwen, GLM, and Kimi, which means developers can iterate on familiar base models without having to pre-train from scratch.

Distributed training is the practice of splitting a large training task across multiple GPUs or even multiple nodes for parallel execution. Common strategies include data parallelism (each GPU processes different data and synchronizes gradients), tensor parallelism (splitting the parameters of a single layer across multiple GPUs), and pipeline parallelism (assigning different layers to different devices). Large-scale RL training also involves alternating execution between inference and training, placing higher demands on communication bandwidth and scheduling. The checkpoint mechanism periodically saves model weights and optimizer states, so that if a task is interrupted, it can resume from the most recent checkpoint, avoiding the loss of days of training results. These engineering details usually account for most of the workload in a post-training project, and handing them to the platform can significantly reduce the burden on teams.

Who Should Use It

The target user profile for this service is fairly clear:

1. Small and medium teams with domain data but lacking training engineering capabilities: They can focus on data and objective design, with the platform covering infrastructure.
2. AI product teams that need rapid experimentation: Compare post-training results across multiple open-source base models to accelerate model selection.
3. Research-oriented developers: They need flexible control over the training loop while avoiding the high cost of building their own clusters.

It is worth noting that the effectiveness of post-training depends heavily on data quality and objective design. The platform solves the problem of "being able to run," not the entirety of "training well."

Signals Worth Watching

Together's move reflects a trend in the AI infrastructure layer: extending from pure inference hosting to the full chain of training and post-training. The prosperity of open-source models makes "model ownership" feasible, and post-training services are the tool that turns this ownership into real competitive advantage. For teams hoping to break free from dependence on closed-source APIs, this is a path worth evaluating.

One-line Summary

Together launches a Post-Training service that lets developers flexibly post-train open-source models.

Paragraph Summary

Together has released a Post-Training service aimed at enterprises' need to shape open-source LLMs into custom models. The service supports two paths—supervised fine-tuning (SFT) and reinforcement learning (RL)—allowing developers to customize data loading, loss functions, and reward computation as if writing a training loop, while distributed training orchestration, GPU cluster management, and checkpoint saving and recovery are handled by the platform. Supported models cover mainstream open-source series such as Qwen, GLM, and Kimi. Target users include small and medium teams with domain data but lacking engineering capabilities, AI product teams that need to quickly compare base models, and research-oriented developers. The article also notes that post-training effectiveness still depends heavily on data quality and objective design; the platform addresses engineering feasibility, not the entirety of training outcomes.