---
license: mit
tags:
- lora
- math-reasoning
- process-reward-model
- topology
language:
- en
- zh
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
pipeline_tag: text-generation
---

# TopoPRM Checkpoints

LoRA adapters trained with TopoPRM (Topology-Aware Process Rewards) for mathematical reasoning.

Paper: EMNLP 2026 (ARR May cycle)
Code: https://github.com/RWLinno/TopoPRM

## Available Checkpoints

| Subdirectory | Base Model | Method | Steps | Key Results |
|-------------|-----------|--------|-------|-------------|
| `sft-dr1-7b-final` | DeepSeek-R1-Distill-Qwen-7B | SFT | 3651 | GSM8K 83.5% baseline |
| `grpo-topoprm-dr1-7b` | DeepSeek-R1-Distill-Qwen-7B | GRPO+TopoPRM | 100 | Hierarchical reward |
| `grpo-topoprm-qwen35-9b` | Qwen3.5-9B | GRPO+TopoPRM | 50 | GSM8K 93.5%, MATH500 49.8% |
| `opd-topoprm-dr1-7b-v2` | DeepSeek-R1-Distill-Qwen-7B | OPD Stage3 | 200 | MATH500 60.8%, Omni-MATH 56.9% |
| `opd-topoprm-qwen35-9b-v2` | Qwen3.5-9B | OPD Stage3 | 50 | Distillation |
| `grpo-scae-qwen35-9b` | Qwen3.5-9B | GRPO+SCAE | 949 | SCAE variant |

## Common LoRA Config

All adapters share:
- **r**: 64
- **alpha**: 128
- **dropout**: 0.05
- **target_modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **task_type**: CAUSAL_LM

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
model = PeftModel.from_pretrained(base_model, "rwlinno/topoprm-ckpts", subfolder="grpo-topoprm-dr1-7b")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
```

## Citation

```bibtex
@inproceedings{topoprm2026,
  title={Topology-Aware Process Rewards for Verifiable Mathematical Reasoning},
  author={Weilin Ruan},
  booktitle={Proceedings of EMNLP 2026},
  year={2026}
}
```