---
license: agpl-3.0
language:
- en
tags:
- mmla
- larc
- memory
- recursive-self-improvement
- custom-code
---

# Orbit · MMLA-Orbit-1B

**Orbit is the first released MMLA model: a 1B-class model with authoritative memory, a learned policy-state initialization, and executable memory-manager adaptation.** MMLA separates the information a model retains from the state that governs how it acts. This checkpoint brings those components together in one model package.

[Runtime](https://github.com/MMLA-org/MMLA-RSI) · [MMLA report](https://arxiv.org/abs/2606.28876v4) · [LARC report](https://arxiv.org/abs/2609.40063) · [MMLA-RSI report](https://arxiv.org/abs/2610.20294v1)

## Architecture

| Component | What it does |
|---|---|
| **MMLA / PDSA** | Separates policy state Φ and authoritative memory M, with explicit memory reads, bounded writes, and joint-action interfaces. |
| **LARC / RTT** | Provides a trained rank-4 residual initialization and problem-local feedback updates, with reset to the saved trained initialization. |
| **RTMA / MMLA-RSI** | Retains arrived experience in M and searches executable memory-management rules. An independently admitted successor can take over the management role. |
| **Frame / Stream / Joint** | Supports complete-frame and causal-stream input, CSBC segmentation, and combined numerical/memory actions. |

MMLA is mixer-agnostic; Orbit uses a Transformer-based mixer with trained MMLA components. The package includes the task reader, trained proposer, original proposer control, independent qualification reader and shared input-memory readout. Its default deployment keeps the trained Φ fixed while using M. Numerical RTT and joint interfaces are available; predictive Q/J heads require qualification for their actual reader and task. Architecture support is separate from evidence of performance gains or successful recursive improvement.

## Quick start

Download the complete repository, then install its pinned runtime:

```sh
python -m pip install -r /path/to/Orbit/requirements.txt
```

```python
from mmla_orbit import load_orbit, load_manager

model = load_orbit("/path/to/Orbit", device="cuda")
manager = load_manager(model, "/path/to/Orbit/deployment.json")
state = model.new_task_state(seed=17, tenant="demo", rule=manager)
```

The host supplies task observations and real feedback through the [task interfaces](https://github.com/MMLA-org/MMLA-RSI/blob/main/docs/orbit/USAGE.md). See the [RSI interface](https://github.com/MMLA-org/MMLA-RSI/blob/main/docs/orbit/RSI_RUNTIME.md) for bounded proposal, evaluation and rule succession. The current default task adapter uses ALFWorld-style observations; additional environments need an appropriate adapter.

## Conventional language benchmarks

| Model | ARC-E | ARC-C | HellaSwag | WinoGrande | MMLU |
|---|---:|---:|---:|---:|---:|
| **Orbit-1B** | 62.08 | 36.18 | 47.44 | 55.17 | 52.06 |
| MiniCPM5-1B-SFT | 61.11 | 35.75 | 47.77 | 56.12 | 53.01 |
| MiniCPM5-1B (RL/OPD) | 60.44 | 36.01 | 47.69 | 56.27 | 52.91 |
| Qwen2.5-1.5B-Instruct | 75.93 | 46.50 | 68.33 | 62.83 | 60.13 |
| SmolLM2-1.7B-Instruct | 62.92 | 43.86 | 71.77 | 68.35 | 48.40 |

Accuracy percentages on the complete 2,376 / 1,172 / 10,042 / 1,267 / 14,042 example panels. Evaluation uses zero-shot completion likelihood, empty M, fixed Φ, no chat template, and FP32 execution of the saved weights. ARC and HellaSwag use character-normalized likelihood; WinoGrande uses common-suffix likelihood; MMLU is micro accuracy over 57 subjects. These previously evaluated panels are regression comparisons. Qwen and SmolLM2 have more parameters. Exact counts, protocol and model identities are in [benchmark results](results/benchmarks.json).

## Agentic evaluation

Orbit uses fixed trained Φ, eight-slot authoritative M and the deployed RTMA manager. Eight external models use their saved chat/tool templates and ordinary conversation history. The table reports complete evaluations of these configured systems; their prompts, context limits, runtimes and hardware differ.

| Model | ALFWorld success | BFCL success | ScienceWorld mean | ScienceWorld success |
|---|---:|---:|---:|---:|
| **Orbit-1B (M + RTMA)** | 41.79% (56/134) | 0.00% (0/200) | -28.28 | 1/90 |
| MiniCPM5-1B-SFT | 0.00% (0/134) | 1.50% (3/200) | -16.50 | 0/90 |
| MiniCPM5-1B | 0.00% (0/134) | 2.00% (4/200) | -8.28 | 0/90 |
| Qwen2.5-1.5B-Instruct | 0.00% (0/134) | 0.50% (1/200) | -25.36 | 1/90 |
| SmolLM2-1.7B-Instruct | 0.00% (0/134) | 0.00% (0/200) | 1.92 | 0/90 |
| LFM2.5-1.2B-Instruct | 0.00% (0/134) | 1.50% (3/200) | 2.92 | 1/90 |
| SmolLM3-3B | 0.00% (0/134) | 3.50% (7/200) | -22.06 | 2/90 |
| Qwen3.5-4B | 3.73% (5/134) | 46.50% (93/200) | -9.31 | 3/90 |
| Qwen3.5-9B | 24.63% (33/134) | 60.50% (121/200) | -24.30 | 8/90 |

ALFWorld is a **134-task seen regression panel**. BFCL covers **200 multi_turn_base cases**. ScienceWorld covers **30 task classes with three fixed test variations each**, scored by the native terminal score (−100 to 100); task successes are shown separately. These panels have been reused during diagnosis. They measure the declared bounded protocols, with no claim of unseen generalization, a full official leaderboard or architecture-only advantage.

**ALF protocol correction.** Orbit now uses the original action instruction defined in its packaged `ALFWorldFormatter`. A complete same-weight comparison found 56/134 successes with that instruction and 35/134 with the evaluation-specific rewrite: 30 tasks succeeded in both arms, 26 only with the original and 5 only with the rewrite. The table follows the packaged interface; both complete arms are retained. External ALF results retain their revised instruction, so this column is not a prompt-matched model comparison. Historical Orbit counts 54/35/36 belong to their original runs; the new comparison does not reconstruct all historical physical states.

**Tool and science failures remain visible.** Orbit BFCL is still 0/200. Complete schema-normalization and pre-calibration-readout comparisons did not restore task success. The ScienceWorld table uses the latest complete revised-instruction arm: −28.28 mean and 1/90 success. The original-instruction arm had −6.41 mean but 0/90 success, mostly avoiding negative terminations; it is retained as a diagnostic. The earlier revised-instruction run had the same mean but 0/90 success and seven different per-task scores. These are separate runs, not values selected per task.

All tasks allow at most 64 replies; ALFWorld/ScienceWorld allow 64 generated tokens per reply and BFCL 512. Orbit contexts are 4096/8192/16384 tokens for ALFWorld/ScienceWorld/BFCL; SmolLM2 uses 8192 and the other external models 32768. BFCL parses saved text replies and returns tool feedback as user observations: `[]` or a parsing rejection ends a user turn, and the 21st nonempty reply triggers the per-turn stop. The exact native tool-call/tool-result messaging protocol is not reproduced. MiniCPM5-1B uses the saved SFT template override. ScienceWorld initializes from the variation ID; the host seed argument is not forwarded, and exact historical JVM-state replay is not established.

The 3392 external endpoints are retained in full, including the independently checked Qwen3.5 BFCL correction to 93/200 and 121/200. That correction permits a prose prelude before a complete native function-call block; the new runs used a local RTX4090 while historical Qwen runs used RTX PRO6000. Nine SmolLM3 BFCL tasks used explicit continuation; unreturned-request compute is incompletely known. Source identities, all 3816 per-task outcomes, limits, precision, continuation records and costs are in the result files. Exact runtime snapshots and raw trajectories remain in the private research archive; the public indices support score recomputation but are not a standalone end-to-end harness release.

No new weights or RSI successors are installed for these results. Numerical RTT and recursive adaptation remain separate evaluation questions.

[Scores, protocols and performance](results/agentic-v6.json) · [Per-task results](results/agentic-tasks-v6.jsonl) · [Both instruction arms](results/agentic-instruction-pairs.json) · [Historical v3](results/agentic-v3.json) · [Earlier protocols](results/agentic.json)

## Files and license

`model.safetensors` contains all model tensors, including omega, qualification and the internal roles. `config.json` records the architecture, role layout and exact runtime version. Tokenizer and generation files are at the repository root. Runtime code is maintained in MMLA-RSI; use `load_orbit` to restore the complete model and its memory interfaces.

The consolidated archive preserves the original tensor values and role identities. It does not require separate adapter weight files. `deployment.json` supplies the packaged memory-management rule, and `MANIFEST.json` records file hashes. Optimizer states, raw task traces and internal reports are outside this inference package.

MMLA additions are licensed under **AGPL-3.0**. Upstream components retain their licenses and attribution in [NOTICE](NOTICE). Please cite the three reports linked above; [CITATION.bib](CITATION.bib) provides BibTeX.