Preprint 2026 · Open Full-Stack Release · OpenRSI First Release
Open weights · open gym · open search — the full OpenMLE stack, released
Live · illustrative simulation, not paper trace. OpenMLE-Evo growing a population of executable programs — node size ∝ simulated validation score. Move your cursor to perturb it.
Project Film · 64 seconds
From executable tasks and execution-grounded learning to evolutionary search and reported results: a visual tour of OpenMLE-Gym, OpenMLE-ERL, OpenMLE-Evo, and Frontis-MA1.
01 · TL;DR
One loop
Draft · Improve · Debug · Crossover — trained with execution-grounded SFT + RL, then composed into long-horizon search. Learning and evolution close.
39.39 → 71.21
Base → post-trained → Evo-Max, MLE-Bench Lite @ 12 h / task on one RTX 4090 capped at 12 GB VRAM. Beats GPT-5.5 + Codex (68.18), approaching GPT-5.6 Sol and the 2.8T Kimi K3.
100% open
Weights · gym · sandbox · training · search · eval. The full stack, released.
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.
02 · Mechanism Ladder
Verified trajectories return to train the model that generates future evolution.
Artifacts evolve. The improver stays frozen.
Experience flows back into the search.
The improver itself is trained — SFT + RL on verified trajectories.
Each system improves the process that builds its successor.
03 · The Open Full-Stack
Sandbox feedback = training signal = search signal.
MODULE 01 — ENVIRONMENT
MODULE 02 — TRAINING
MODULE 03 — INFERENCE
Trained by OpenMLE. Driving OpenMLE. Evaluated only on third-party benchmarks.
PRODUCT × ENGINE04 · Trainable Atomic Operators
Invoked thousands of times per task — repair, refine, recombine.
① Evolutionary Inference — operator-driven expansion of the solution tree
Scaffold-agnostic → Greedy · abMCTS · AIRA-EVO · OpenEvolve
05 · MLE-Bench Lite · 12 h / task · 1× RTX 4090 · 12 GB VRAM cap · avg of 3 runs
Gains compose everywhere: 30B 34.85 → 53.03 → 66.67 (+18.18pp) · GLM-5.2 59.09 → 66.67 · MiniMax M3 54.55 → 65.15 · Kimi K2.6 59.09 → 66.67 · medal mix shifts toward Gold — matching Kimi K3's Gold rate.
AIRA-Evo baseline · tokens / 12h run
129.3M
Free-form history — every expansion gets more expensive.
OpenMLE-Evo · same checkpoint, seed & budget
75.3M −41.7%
Prompt −50.3% · nodes −12.4% · more new-best updates.
0.9455held-out Human Rank
Debug → Crossover → ConvNeXt leap. 85.0% of the gain from late ops. Best baseline: 0.6303, no medal.
0.8889held-out Human Rank
Memory keeps winning branches, flags a weak ResNet50. 91.9% of the gain from Improve + Crossover. Best baseline: 0.2963, no medal.
06 · RL Training Curves · Frontis-MA1-35B
The rollout reward is noisy by design, while held-out validation and medal count expose the underlying learning trajectory. Hover the chart for exact checkpoint values.
221
RL policy updates logged
0.180 → 0.400
Rollout reward · first to final step
0.404
Peak validation base reward
31 /176
Peak validation medal count · out of 176 tasks
Reward / medals
stitched training step
07 · Meta-Ability & Transfer
NatureBench Lite, controlled: hold the model or the framework fixed — both transfer.
Model transfer · framework fixed · Match-SOTA
Same framework, Frontis-MA1 swapped in.
Framework transfer · model fixed · Match-SOTA
Same model, OpenMLE-Evo search swapped in.
5 / 5
Every modality group improves. No Medal Rate drops.
+2 +4 +1 +4 +3
14 new medals, spread across all five modalities.
24.2 → 34.8
Test medal rate with upper-tail reward shaping (×4.0).
08 · Open-Source Release
R-01
30B & 35B post-trained checkpoints on HuggingFace.
R-02
Audited task inventory + category-specific release artifacts.
R-03
Scheduler + CPU/GPU Docker workers; control / execution / storage separated.
R-04
Execution-grounded SFT data construction + asynchronous RL configs.
R-05
Experience cards, 3-factor selection, operator-conditioned context.
R-06
Full MLE-Bench Lite / NatureBench Lite evaluation & reproduction scripts.
@misc{openrsi2026frontisma1,
title = {Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in ML Engineering},
author = {{OpenRSI Team}},
year = {2026},
url = {https://github.com/FrontisAI/OpenRSI},
note = {Preprint}
}