With the smartest person I know, @HarshSensei, we're building evsys-sdk for the community, this open-source repository allows anyone to buil…

With the smartest person I know, @HarshSensei, we're building evsys-sdk for the community, this open-source repository allows anyone to build their own continual learning system with first-class support for @FireworksAI_HQ. We believe continual learning will not be a single algorithm, but rather an auto-research system that looks at production traces, figures out the best way to update the model weights by conducting experiments on both data and algorithms, and runs evaluations to measure the results of those experiments. You can now create a system with one deploy command which pulls in your production traces with some frequency, triggers the ML auto-research loop automatically, which uses any Tinker-compatible training API (@FireworksAI_HQ) to conduct experiments and train your model from the deployed checkpoint, and finally updates it with the better model on inference providers. RL, SFT, self-distillation, custom algorithms, etc. are tools that the auto-research system has at its disposal. One of the major issues blocking auto-research for ML experimentation is that training code requires too much plumbing, which fills up the agent's context and is non-standardized across experiments, making it difficult to compare experiments and learn from past experiments. The SDK provides an auto-research harness which takes away the complexity of training code and squashes each experiment down to just a config file, while giving full freedom to run any data or algorithm experiment. This way, the auto-research agent doesn't suffer from context bloat and performs much better than standard auto-research harnesses. The SDK is also highly extensible and customizable, providing simple abstractions for curating any form of data, running any algorithm, pulling from any trace database, and running training on any Tinker-compatible backend. We see auto-research as the only solution to continual learning, and this open-source contribution as a way to democratize access to it and push the usage of open-source models. As a roadmap to enable ML auto-research and continual learning, there is a lot of work to be done: 1. Indexing previous experiments and existing plethora of research to give to the auto-research agent so it keeps improving and learns from past experiments. 2. Making app-layer integrations, e.g. one-click distillation from your traces generated through Claude Code, Cursor, etc. into an open-weights model. 3. Improving the auto-research harness and adding more integrations.

Source: Fireworks AI (X) | 2026-08-12

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