dagger-4B_SFT_GRPO

DAGGER-4B-SFT-GRPO

<p align="center"> <a href="https://arxiv.org/abs/2601.06853"> <img src="https://img.shields.io/badge/%F0%9F%94%A5_Accepted_at-EMNLP_2026_(Findings)_%F0%9F%94%A5-b12a00?style=for-the-badge&labelColor=ffb300" alt="Accepted at EMNLP 2026 (Findings)"> </a> </p>

Model Description

DAGGER-4B-SFT-GRPO is the smaller variant of DAGGER, trained with SFT followed by GRPO on Gemma-3-4B. While showing lower performance than the 12B variant, it demonstrates that the DAGGER framework can work with smaller models.

Highlights

• Lightweight: 4B parameters for resource-constrained deployment
• SFT → GRPO training: Full training pipeline
• Clear gain over 4B SFT: +3.0 weighted accuracy (44.3 → 47.3). Note this 4B model is less distractor-robust than the stronger CoT baselines (it drops 23.4 / 27.4 points); the 12B model is the robust one
• Capacity study: Demonstrates model size requirements for graph generation

Model Overview

Performance

Comparison with 12B Variant

Key Finding: The 12B model provides +22 points improvement, suggesting a capacity threshold for effective computational graph generation.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "dipta007/dagger-4B_SFT_GRPO"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

USER_PROMPT_TEMPLATE = """You are an expert Bengali Math Reasoner. Your task is to solve mathematical problems by constructing a "Computational Graph".

### Graph Rules:
- `id`: Unique identifier (e.g., "n1", "n2").
- `val`: The raw number extracted from text (for input nodes).
- `op`: The operation (`add`, `sub`, `mul`, `div`, `round`, `sqrt`, `floor`, `sum`, `mean`). Use `const` for input numbers.
- `args`: List of input node IDs.
- `distractor`: Boolean (`true` / `false`). Set to `true` if the node is NOT used in the final calculation path.
- `label`: Label for the node.

### Available Operations:
- Input: `const` (Use this for all numbers found in text or constants).
- Arithmetic: `add`, `sub`, `mul`, `div`, `abs` (absolute difference).
- Logic/Stats: `sum`, `mean`, `min` (minimum), `max` (maximum).
- Rounding: `round` (nearest int), `floor` (round down), `ceil` (round up).
- Advanced: `sqrt`, `pow`, `mod` (remainder), `gcd`, `lcm`.
- Output: `identity` ("final_result" points to the answer node)

Only output a JSON graph representing the solution, nothing else. Nodes must be topologically sorted, and there must be exactly one "final_result" node that represents the final answer. One example is provided below.

### Example:
Question:
মিনার কাছে ১২২১৯৫ টা কলম আছে। রাজুর কাছে ২৫০৮৪ টা কলম আছে। মিনা রাজুর কাছে ১১২৬ টি কলম চাইল। রাজু ১০০০ টি কলম দিতে রাজি হল, কিন্তু পরে আর দিলেনা। প্রতিটি কলমের দাম ৪৫.৬ টাকা। মিনা যদি কলমগুলো বিক্রি করতে চায়, সে কত টাকা পাবে?

Output:
```json
{{
  "nodes": [
    {{"id": "n1", "op": "const", "val": 122195, "distractor": false, "label": "মিনার কলম"}},
    {{"id": "n2", "op": "const", "val": 25084, "distractor": true, "label": "রাজুর কলম"}},
    {{"id": "n3", "op": "const", "val": 1126, "distractor": true, "label": "মিনা রাজুর কাছে চাইল"}},
    {{"id": "n4", "op": "const", "val": 1000, "distractor": true, "label": "রাজু দিতে রাজি হল"}},
    {{"id": "n5", "op": "const", "val": 45.6, "distractor": false, "label": "প্রতিটি কলমের দাম"}},
    {{"id": "total_money", "op": "mul", "args": ["n1", "n5"], "distractor": false, "label": "মিনার মোট টাকা"}},
    {{"id": "final_result", "op": "identity", "args": ["total_money"], "distractor": false, "label": "চূড়ান্ত উত্তর"}}
  ]
}}```

### Your Task:

Question:
{question}

Output:
"""

question = "রজারের 5টি টেনিস বল আছে। সে আরও 2 ক্যান টেনিস বল কিনেছে। প্রতিটি ক্যানে 3টি করে টেনিস বল আছে। তার কাছে এখন কতগুলি টেনিস বল আছে?"
prompt = USER_PROMPT_TEMPLATE.format(question=question)

messages = [
  {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, top_p=0.8)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)

print(response)

Training Configuration

Same as 12B variant:

When to Use This Model

• Resource-constrained deployment: When 12B is too large
• Capacity studies: Research on model size vs. performance
• Edge deployment: Smaller memory footprint
• Prototyping: Faster iteration during development

Limitations

• Lower accuracy: 22 points below 12B variant
• Reduced robustness: Larger accuracy drop under distractors
• Capacity constraints: May struggle with complex multi-step problems

Related Models

License and Data Provenance

Model weights are released under the Gemma Terms of Use.

Training data is not fully permissive. Part of the SFT data and all GRPO prompts come from numina-math-cot-bn, which is CC BY-NC-SA 4.0 (NonCommercial, ShareAlike). For commercial use, re-derive that portion from the Apache-2.0 upstream AI-MO/NuminaMath-CoT.

Citation

@inproceedings{nazi2026dagger,
  title={{\dag}DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems},
  author={Zabir Al Nazi and Shubhashis Roy Dipta and Sudipta Kar},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2026},
  year={2026},
  eprint={2601.06853},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2601.06853},
}