走进模型工厂：Poolside 如何把模型训练变成可复用的工业系统

Poolside treats the model factory, rather than any single heroic training run, as the durable source of model progress.

Streaming data directly into training turns many dataset-mixing experiments from slow rematerialization work into configuration changes.

An immutable data layer lets researchers trace a model outcome back to the exact data sources and transformations that produced it.

Researchers increasingly supervise agents that inspect data, launch experiments, and evaluate model runs instead of performing every step manually.

Poolside expects reinforcement learning to move earlier in the training pipeline instead of remaining only a final post-training step.

Poolside focuses on coding and long-horizon software tasks because they require reasoning, tools, external data, and persistent interaction with an environment.

As models become more agentic, evaluations must measure the model together with the harness, tools, prompts, and data access that shape its behavior.

Public benchmarks are useful for comparison, but teams need internal evaluations built from the work their users actually perform.

Architecture, precision, networking, and data choices are ultimately judged by how efficiently they convert compute into useful capability.

Meaningful openness requires experiments, datasets, optimizers, post-training recipes, and evaluation details, not only released weights.