ArXiv

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

2026-07-30 · 17:34 UTC ·Junlin Yang, Che Jiang, Yu Fu... ·1 min read
Authors
Junlin Yang, Che Jiang, Yu Fu...
Categories
cs.CL
arXiv
https://arxiv.org/abs/2607.28568v1
PDF
https://arxiv.org/pdf/2607.28568v1

Brief

Frontis-MA1 (35B) and the OpenMLE stack create an executable AI4AI platform to study recursive self-improvement in machine-learning engineering. The team post-trains a 35B meta-evolution agent with execution-grounded SFT and RL on four operators (Draft, Improve, Debug, Crossover) and composes them into long-horizon evolutionary search. Under constrained compute (12h per task, single RTX 4090), Frontis-MA1 improves MLE-Bench Lite Medal Average from 39.39% to 60.61% (71.21% with Evo-Max), transfers to NatureBench Lite, and is released with code and weights for reproducible RSI research.

Why it matters

Introduces OpenMLE, a full-stack executable AI4AI testbed (OpenMLE-Gym, OpenMLE-RL, OpenMLE-Evo) and post-trains Frontis-MA1 (35B) as a meta-evolution agent; training aligns execution-grounded SFT and RL on four atomic program-evolution operators: Draft, Improve, Debug, Crossover, with data deduplicated against evaluation benchmarks.

Key details

  • On MLE-Bench Lite under a 12-hour per-task budget on one RTX 4090 (capped at 12 GB VRAM), Frontis-MA1 raises Medal Average from 39.39% to 60.61% using OpenMLE-Evo and to 71.21% with OpenMLE-Evo-Max (benchmark-independent priors + asynchronous search), reportedly exceeding GPT-5.5+Codex and approaching GPT-5.6 Sol and 2.8T Kimi K3.
  • Demonstrates transfer on held-out NatureBench Lite: with the framework fixed, swapping in the trained model increases Match-SOTA from 50% to 70%; with the model fixed, swapping in OpenMLE-Evo raises it from 20% to 50%. The authors release model weights and the full OpenMLE stack (https://github.com/FrontisAI/OpenRSI).
Source evidence

Abstract

Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce OpenMLE, an open full-stack system for RSI research in MLE, spanning verifiable task environments with execution feedback (OpenMLE-Gym), operator learning (OpenMLE-RL), and long-horizon search (OpenMLE-Evo). On this stack we post-train Frontis-MA1 (35B) as a meta-evolution agent for MLE, aligning post-training and inference around four atomic program-evolution operators (Draft, Improve, Debug, Crossover): the same operators are trained via execution-grounded SFT and RL on data deduplicated against all evaluation benchmarks, then composed into long-horizon search, coupling learning and evolution in a single loop. On MLE-Bench Lite under a 12-hour per-task budget on one RTX 4090 capped at 12 GB VRAM, Frontis-MA1 (35B) improves Medal Average from 39.39% to 60.61% over its base model with OpenMLE-Evo, and reaches 71.21% with OpenMLE-Evo-Max (benchmark-independent experience priors and asynchronous search), exceeding GPT-5.5 + Codex and approaching GPT-5.6 Sol and the 2.8T Kimi K3. On held-out NatureBench Lite, both components transfer: with the framework fixed, swapping in the trained model raises Match-SOTA from 50% to 70%; with the model fixed, swapping in OpenMLE-Evo raises it from 20% to 50%. We release the model weights and the full OpenMLE stack to enable reproducible research on executable AI4AI toward RSI. Code: https://github.com/FrontisAI/OpenRSI