Bringing RL environments to Fireworks AI
We’re excited to announce that we’re working with Fireworks AI.
Fireworks has made it easy for teams to serve and train open models: fast inference, fine-tuning, and reinforcement fine-tuning, all on one platform. The infrastructure to run RL is no longer the hard part. The hard part is the RL environment: the tasks, the verifiers, and the rubrics that define what “better” actually means for a model. That is what we do at Abundant, and we’re excited to work with Fireworks to bring the two together.
Training is getting easier. Environments aren’t.
A model trained with RL only gets as good as the environment it learns from. If a task is ambiguous, the model learns the ambiguity. If a verifier can be gamed, the model learns to game it. We’ve written about how often frontier models find shortcuts when the environment lets them, and about how much care goes into designing a good task in the first place.
Building good environments is manually intensive work. It takes smart researchers, good taste, and a lot of time spent looking at data. Until recently, this kind of work was mostly reserved for frontier labs. But more and more companies want to own their own intelligence: to take their own data and workflows and turn them into a model that is better at the things they care about. For those teams, the environment is the bottleneck.
How it works
Fireworks customers who want to post-train on their own workflows can work with Abundant to build the environments for it. In practice, that means:
- Environments built from real data. We work directly with each team to turn their data, tools, and domain knowledge into tasks that reflect the work they want their model to do.
- Verifiers and rubrics that hold up. We audit every environment for quality before it is used for training, and look for the ways a model could pass without actually solving the task.
- Train and serve on Fireworks. Once the environments are ready, teams can run training and deploy the resulting model on Fireworks.
- A loop, not a handoff. What we learn from training runs goes back into the environments, so they keep improving as the model does.
Why we’re excited
Fireworks gives teams the infrastructure to train and serve models. Abundant gives them the environments that make that training worthwhile. Together, we can take a team from its own data to a better model, without the team needing to build an RL research group from scratch.
You can already run Abundant tools on your own Fireworks account. See Bring Your Own Key: Fireworks AI for setup.
If you’re building on Fireworks and want to train a model on your own workflows, we’d love to talk.