Frontis-MA1 open-sources 35B recursive self-improvement ML agent

A group of researchers just posted an open recursive self-improvement setup for machine learning engineering, and the specifics are what make it worth reading rather than skimming. Frontis-MA1 is a 35 billion parameter agent, released with weights and the surrounding stack, that its authors say lifts the Medal Average on MLE-Bench Lite from 39.39% to 60.61% over the base model, and up to 71.21% with a fuller search configuration. The paper is on arXiv, with code and weights at FrontisAI/OpenRSI on GitHub.

The interesting bit is the pitch, not just the score. Recursive self-improvement has mostly lived in two places, theory papers and closed labs where you cannot see the training loop. This release goes the other way. The authors put out OpenMLE-Gym for verifiable task environments, OpenMLE-RL for operator learning, and OpenMLE-Evo for long-horizon search, and align post-training and inference around four named operators, Draft, Improve, Debug, Crossover, that get trained end-to-end and then composed into a single loop. If those pieces work as described, someone outside the lab can, in principle, re-run the whole thing.

The compute framing is also unusually modest for a story like this. Each MLE-Bench Lite task ran under a 12-hour budget on a single RTX 4090 capped at 12 GB of VRAM, which is a lot closer to what an academic group actually has than the multi-node setups these numbers usually come with. On the held-out NatureBench Lite, the paper reports that swapping in the trained model raises Match-SOTA from 50% to 70%, and swapping in the search framework raises it from 20% to 50%.

The honest caveat is that all of this is self-reported, single-paper, and measured on benchmarks the same team scoped. The claim that Frontis-MA1 exceeds GPT-5.5 + Codex and approaches GPT-5.6 Sol and the 2.8T Kimi K3 is theirs, not an independent one, and the write-up does not detail the post-training compute bill or the license attached to the weights. Take the specifics as reported, not settled.

Even with those caveats, an open, downloadable RSI stack that runs on hobbyist-grade hardware is the kind of release that changes what a small research group can attempt this quarter. If the numbers survive replication, hosted MLE-copilot vendors will have to explain what their offering does that a locally-run 35B agent does not.