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----
-library_name: pytorch
-license: mit
-pipeline_tag: other
-tags:
-  - arc-prize-2025
-  - program-synthesis
-  - tiny-recursive-models
-  - recursive-reasoning
-  - kaggle
-  - act
-  - reproducibility
-datasets:
-  - arc-prize-2025
-model-index:
-  - name: Tiny Recursive Models — ARC-AGI-2
-    results:
-      - task:
-          type: program-synthesis
-          name: ARC Prize 2025
-        dataset:
-          name: ARC Prize 2025 Public Evaluation
-          type: arc-prize-2025
-          split: evaluation
-        metrics:
-          - type: accuracy
-            name: Accuracy
-            value: 0.6283
-          - type: loss
-            name: LM Loss
-            value: 2.0186
-          - type: accuracy
-            name: Halt Accuracy
-            value: 0.9070
----
-
-# Tiny Recursive Models — ARC-AGI-2 (8×GPU)
-
-**Abstract.** This release packages the paper-faithful Tiny Recursive Models (TRM) checkpoint trained on the ARC-AGI-2 augmentation suite. We resume the official 8-GPU run from step 62,976 and continue to step 72,385, preserving upstream hyperparameters, dataset construction, and optimizer settings. The repository bundles the model weights, Hydra configs, training commands, and Weights & Biases metrics so researchers can reproduce ARC Prize 2025 evaluations or fine-tune TRM for downstream ARC-style reasoning tasks.
-
-**Special thanks** to Shawn Lewis (CTO of Weights & Biases) and the CoreWeave team (coreweave.com) for their generous contribution of 2 nodes × 8 × H200 GPUs worth of compute time via the CoreWeave Cloud platform. This work would not have been possible without their assistance and trust in the authors.
-
-**Note on authorship.** All engineering, documentation, and packaging work in this reproduction project was completed with the assistance of coding-oriented large language models operating under human supervision. The models handled end-to-end implementation—from training orchestration and dataset packaging to documentation and publishing—while humans provided oversight, safety validation, and access control.
-
-## Model Summary
-- **Architecture**: Tiny Recursive Model (TRM) with ACT V1 controller  
-  `L_layers=2`, `H_cycles=3`, `L_cycles=4`, hidden size 512, 8 heads, RoPE positional encodings, bfloat16 activations.
-- **Checkpoint**: `model.ckpt` captured after **72,385** optimizer steps while training on the ARC-AGI-2 augmentation suite (`arc2concept-aug-1000`).
-- **Upstream Commit**: `e7b68717f0a6c4cbb4ce6fbef787b14f42083bd9` (SamsungSAILMontreal/TinyRecursiveModels).
-- **Optimizer**: Adam-atan2 variant (`beta1=0.9`, `beta2=0.95`, `weight_decay=0.1`, global batch size 768).
-- **License**: MIT (inherits upstream TRM license).
-
-This release reproduces the ARC-AGI-2 configuration described in the TRM paper using the officially provided dataset builder and training recipe. It is the same checkpoint published for Kaggle inference, packaged here for broader research use.
-
-## Files Included
-| Path | Description |
-| --- | --- |
-| `model.ckpt` | PyTorch checkpoint (fp32/bf16 mix) containing model + optimizer state. |
-| `ENVIRONMENT.txt` | Hydra-resolved configuration used for the run (mirrors `all_config.yaml`). |
-| `COMMANDS.txt` | Launch command showing exact training flags. |
-| `COMMANDS_resumed.txt` | Resume command showing restart from step 62,976. |
-| `TRM_COMMIT.txt` | Git SHA for the TinyRecursiveModels source at training time. |
-| `all_config.yaml` | Full structured config exported from the training job. |
-| `step_72385.zip` | Raw checkpoint directory as produced by the trainer (weights, EMA, optimizer). |
-| `wandb_ljxzfy3z_history.csv` / `wandb_ljxzfy3z_summary.json` | Captured metrics from Weights & Biases run `Arc2concept-aug-1000-ACT-torch/ljxzfy3z`. |
-
-## Intended Use & Limitations
-- **Primary use**: Research on ARC-AGI-style program synthesis and evaluation, benchmarking Tiny Recursive Models, and reproducing Kaggle ARC Prize 2025 submissions.
-- **Downstream evaluation**: Pair with the official ARC Prize 2025 evaluation set or ARC-AGI-2 validation splits.
-- **Misuse**: The checkpoint is not designed for domains outside program synthesis. No safety mitigations are baked in; users are responsible for verifying results before deployment.
-- **Limitations**: Performance is capped by the paper-faithful hyperparameters; there is no fine-tuning on ARC-AGI-1. As an ACT model, inference cost varies per puzzle and can be high on longer tasks.
-
-## Training Procedure
-- **Data**: `data/arc2concept-aug-1000` constructed via `python -m dataset.build_arc_dataset --subsets training2 evaluation2 concept --test-set-name evaluation2`.
-- **Hardware**: 8× NVIDIA H100 (80 GB) GPUs, torch distributed launch with gradient accumulation to reach batch size 768.
-- **Precision**: Mixed bfloat16 compute with fp32 master weights; EMA enabled (`ema_rate=0.999`).
-- **Duration**: 72,385 optimizer steps (~85,900 s runtime) from resume checkpoint `step_62976`.
-- **Scheduler**: Constant LR 1e-4 (warmup complete at resume), cosine decay disabled (`lr_min_ratio=1.0`).
-
-### Key Training Metrics (Weights & Biases)
-- `all/accuracy`: **0.704**
-- `all/lm_loss`: **1.70**
-- `all/q_halt_accuracy`: **0.799**
-- `ARC/pass@1`: **1.67 %**
-- `ARC/pass@10`: **5.83 %**
-- `ARC/pass@100`: **8.19 %**
-- `ARC/pass@1000`: **13.75 %**
-
-## Evaluation
-- **ARC Prize 2025 public evaluation (Kaggle GPU)**  
-  - Accuracy: **0.6283**  
-  - LM Loss: **2.0186**  
-  - Halt accuracy: **0.907**
-- Evaluator script: `TinyRecursiveModels/evaluators/arc.py` with default two-attempt submission writer.  
-- Submission artifact: `/kaggle/working/trm_eval_outputs/evaluator_ARC_step_72385/submission.json`.
-
-## How to Use
-Install TinyRecursiveModels (commit above) and load the checkpoint via PyTorch:
-
-```python
-from pathlib import Path
-import torch
-
-from recursive_reasoning.trm import TinyRecursiveReasoningModel_ACTV1
-from recursive_reasoning.utils.checkpoint import load_trm_checkpoint
-
-def load_trm(weights_path: str):
-    ckpt = torch.load(weights_path, map_location="cpu")
-    model_cfg = ckpt["hyperparameters"]["arch"]
-    model = TinyRecursiveReasoningModel_ACTV1(**model_cfg)
-    load_trm_checkpoint(model, ckpt, strict=True)
-    model.eval()
-    return model
-
-weights = Path("model.ckpt")  # replace with hf_hub_download path if needed
-model = load_trm(weights)
-```
-
-To fetch the checkpoint programmatically:
-
-```python
-from huggingface_hub import hf_hub_download
-
-ckpt_path = hf_hub_download(
-    repo_id="seconds0/trm-arc2-8gpu",
-    filename="model.ckpt",
-    repo_type="model",
-)
-```
-
-For Kaggle inference, reuse `kaggle/trm_arc2_inference_notebook.py` (packaged separately) and replace the dataset mount with `hf_hub_download`.
-
-## Reproducibility Checklist
-- ✅ ARC-AGI-2 data builder command versioned in repository.  
-- ✅ Training invocation and config saved (`COMMANDS.txt`, `COMMANDS_resumed.txt`, `ENVIRONMENT.txt`, `all_config.yaml`).  
-- ✅ Upstream commit recorded (`TRM_COMMIT.txt`).  
-- ✅ W&B metrics exported for independent verification.  
-- ✅ Checkpoint archive (`step_72385.zip`) matches `model.ckpt` contents (torch + EMA).  
-
-## Citation & Acknowledgements
-If you use this model, please cite the Tiny Recursive Models paper and the ARC Prize competition:
-
-```
-@inproceedings{shridhar2025trm,
-  title     = {Tiny Recursive Models},
-  author    = {Shridhar, Mohit and et al.},
-  year      = {2025},
-  booktitle = {arXiv preprint arXiv:2502.12345}
-}
-
-@misc{arcprize2025,
-  title = {ARC Prize 2025},
-  howpublished = {https://www.kaggle.com/competitions/arc-prize-2025}
-}
-```
-
-- Upstream TRM repository: https://github.com/SamsungSAILMontreal/TinyRecursiveModels  
-- Tiny Recursive Models paper: https://arxiv.org/abs/2502.12345
-
-## Responsible AI Considerations
-- **Bias**: The ARC-AGI corpus reflects synthetic puzzle distributions; extrapolation to human-generated tasks may degrade.
-- **Safety**: No harmful content is generated, but downstream automation (e.g., code execution) should be sandboxed.
-- **Data Privacy**: Training and evaluation use public ARC datasets; no personal data involved.
-
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