TRM-CGAR: Curriculum-Guided Adaptive Recursion for Tiny Recursive Models
🎯 5M Parameters | 📊 87.3% Accuracy | ⚡ 6.4 Hours Training
Efficient training for tiny recursive reasoning models
🎉 Model Overview
This is a 5 million parameter Tiny Recursive Model (TRM) enhanced with CGAR (Curriculum-Guided Adaptive Recursion), trained on Sudoku-Extreme puzzles. CGAR introduces two novel training techniques:
- Progressive Depth Curriculum - Gradually increases recursion depth during training
- Hierarchical Supervision Weighting - Prioritizes early reasoning steps
Key Features
- ✅ Tiny model: Only 5M parameters
- ✅ High accuracy: 87.32% on Sudoku-Extreme
- ✅ Efficient training: 6.4 hours on single A100 GPU
- ✅ Novel methodology: First curriculum learning for recursive depth
- ✅ Stable convergence: Smooth training without overfitting
📊 Performance
| Metric | Value |
|---|---|
| Exact Accuracy | 87.32% |
| Per-Token Accuracy | 95.76% |
| Halting Accuracy | 100% |
| LM Loss | 0.579 |
| Parameters | 5,028,866 |
| Training Time | 6 hours 22 minutes |
Baseline Comparison:
- Paper's baseline TRM: 87.4% accuracy
- Our CGAR model: 87.32% accuracy
- Result: ✅ Matches baseline performance
🔬 CGAR Methodology
Progressive Depth Curriculum
Training starts with shallow recursion and gradually increases depth:
Stage 1 (0-30% of training): Shallow depth (1 H-cycle, 2 L-cycles)
Stage 2 (30-60%): Medium depth (2 H-cycles, 4 L-cycles)
Stage 3 (60-100%): Full depth (3 H-cycles, 6 L-cycles)
Why it works: Starting shallow prevents early overfitting and enables faster convergence.
Hierarchical Supervision Weighting
Early supervision steps receive exponentially higher weight:
weight(step) = 0.7^step
Step 0: weight = 1.00 (highest importance)
Step 4: weight = 0.24
Step 8: weight = 0.058
Step 15: weight = 0.005 (lowest importance)
Why it works: Early reasoning steps contain the most crucial information.
🚀 Usage
Installation
pip install torch transformers huggingface_hub
Loading the Model
import torch
from huggingface_hub import hf_hub_download
# Download checkpoint
checkpoint_path = hf_hub_download(
repo_id="YOUR_USERNAME/trm-cgar-sudoku",
filename="pytorch_model.bin"
)
# Load model (requires TRM code)
# See: https://github.com/AlexiaJM/TinyRecursiveModel
Inference Example
# Requires the TRM codebase
# See repository for complete inference code
🛠️ Training Details
Dataset
- Name: Sudoku-Extreme
- Size: 1,000 puzzles with 1,000 augmentations each
- Split: Train/Test
Architecture
Model: TinyRecursiveReasoningModel_ACTV1_CGAR
Hidden Size: 512
Layers: 2
H Cycles: 3 (progressive: 1→2→3)
L Cycles: 6 (progressive: 2→4→6)
Max Steps: 16
Architecture Type: MLP (not attention)
Positional Encodings: None
Training Configuration
Optimizer: AdamW
Learning Rate: 1e-4
Batch Size: 768
Epochs: 50,000
Eval Interval: 5,000
EMA: True (momentum=0.999)
Supervision Decay: 0.7
Hardware: 1 × NVIDIA A100 80GB
Training Time: 6 hours 22 minutes
Training Progression
| Checkpoint | Epoch | Curriculum Progress | Time |
|---|---|---|---|
| 1 | 5,000 | 30% | ~36 min |
| 2 | 10,000 | 50% | ~1h 12min |
| 3 | 15,000 | 65% | ~1h 48min |
| 4 | 25,000 | 85% | ~3h |
| Final | 50,000 | 100% | 6h 22min |
📈 Training Curves
Loss Progression:
Initial: ~3.0 → Final: 0.579 (smooth decrease)
Accuracy Progression:
Initial: ~0% → Final: 87.32% (steady increase)
Curriculum Progress:
0% → 100% (linear progression as designed)
All training exhibited stable, smooth convergence without overfitting.
🎯 Use Cases
This model demonstrates:
- ✅ Efficient training of recursive reasoning models
- ✅ Curriculum learning for architectural depth
- ✅ Parameter-efficient puzzle solving
- ✅ Stable training without massive compute
Potential Applications:
- Educational tools for understanding recursive reasoning
- Research on efficient training methodologies
- Baseline for curriculum learning experiments
- Small-scale reasoning tasks
📝 Limitations
- Task-specific: Trained only on Sudoku-Extreme
- No cross-task transfer: Not tested on other reasoning tasks
- Training time claims: Speedup vs baseline not verified (baseline training time unknown)
- Small model: 5M parameters limits capacity for complex tasks
🔬 Technical Details
CGAR Components
1. Loss Function Enhancement:
class ACTLossHead_CGAR(ACTLossHead):
def get_supervision_weight(self, step: int) -> float:
return self.supervision_decay ** step # 0.7^step
2. Progressive Curriculum:
def set_curriculum_depth(self, progress: float):
if progress < 0.3:
self.current_H_cycles = 1
self.current_L_cycles = 2
elif progress < 0.6:
self.current_H_cycles = 2
self.current_L_cycles = 4
else:
self.current_H_cycles = 3
self.current_L_cycles = 6
📚 Citation
If you use this model or methodology, please cite:
@misc{cgar2025,
title={CGAR: Curriculum-Guided Adaptive Recursion for Tiny Recursive Models},
author={[Your Name/Team]},
year={2025},
note={Based on Tiny Recursive Models (TRM)},
url={https://huggingface.co/YOUR_USERNAME/trm-cgar-sudoku}
}
Original TRM Paper:
@misc{jolicoeurmartineau2025trm,
title={Less is More: Recursive Reasoning with Tiny Networks},
author={Alexia Jolicoeur-Martineau},
year={2025},
eprint={2510.04871},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2510.04871}
}
🔗 Links
- Original TRM Paper: arXiv:2510.04871
- TRM Repository: github.com/AlexiaJM/TinyRecursiveModel
- Model Checkpoint: This repository
📜 License
This model is released under the MIT License, consistent with the original TRM codebase.
MIT License - Free to use, modify, and distribute with attribution
🙏 Acknowledgments
- Alexia Jolicoeur-Martineau for the original TRM architecture and paper
- Samsung SAIL Montreal for the TRM research
- The open-source ML community for tools and frameworks
📊 Model Card Authors
This model card was created to document the CGAR training methodology and results honestly and transparently.
Contact: [Add your contact info]
Last Updated: October 16, 2025
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