DAGGER-4B-SFT-GRPO

Name: dagger-4B_SFT_GRPO
Availability: InStock

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

Attribute	Value
Base Model	Gemma-3-4B-Instruct
Training	SFT → GRPO
Parameters	4B
LoRA Rank	64

Performance

Dataset	Original	+Distractor	Drop
MGSM	54.8	31.4	23.4
MSVAMP	70.3	42.9	27.4
Weighted Avg	-	-	47.3

Comparison with 12B Variant

Model	Params	Weighted Avg
dagger-4B_SFT_GRPO	4B	47.3
dagger-12B_SFT_GRPO	12B	69.4 (+22.1)

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

Quickstart

python

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:

Parameter	Value
LoRA Rank / Alpha	64 / 128
SFT Batch Size	256
GRPO Batch Size	32
Generations per Prompt	8
Epochs	4

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

Model	Size	Weighted Avg
dagger-4B_SFT_GRPO	4B	47.3
dagger-4B_SFT	4B	44.3
dagger-12B_SFT_GRPO	12B	69.4

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

bibtex

@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},
}