GanitLLM-0.6B_CGRPO

GanitLLM-0.6B_CGRPO

<p align="center"> <a href="https://arxiv.org/abs/2601.06767"> <img src="https://img.shields.io/badge/%F0%9F%94%A5_Accepted_at-ACL_2026_(Findings)_%F0%9F%94%A5-b12a00?style=for-the-badge&labelColor=ffb300" alt="Accepted at ACL 2026 (Findings)"> </a> </p>

Highlights

GanitLLM-0.6B_CGRPO is a Bengali mathematical reasoning model trained with Curriculum-GRPO directly on the base model (without SFT). This variant shows limited improvement at this scale. Key results:

• +8.8 accuracy on Bn-MGSM benchmark (8.4 → 17.2)
• +23.0 accuracy on Bn-MSVAMP benchmark (12.2 → 35.2)
• 11.67% Bengali reasoning (similar to base model)
• 34.9% fewer tokens in generated solutions (1265 → 824 words)

Note: This model shows limited gains at the 0.6B scale. For better performance, use GanitLLM-0.6B_SFT_CGRPO or consider larger models.

Model Overview

Training Details

This model was trained with a single-stage pipeline:

1. Curriculum-GRPO: Reinforcement learning with difficulty-aware sampling directly on the base model using GANIT-RLVR (~7.3k examples)

Reward Functions

• Format Reward: Validates <think> and <answer> tag structure
• Correctness Reward: +2.0 for Bengali answer match, +1.0 for English match
• Bengali Reasoning Reward: Ensures >80% Bengali text in reasoning

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "dipta007/GanitLLM-0.6B_CGRPO"

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

problem = "একটি দোকানে ১২টি আপেল আছে। যদি ৫টি আপেল বিক্রি হয়, তাহলে কতটি আপেল বাকি থাকবে?"

prompt = f"""A conversation takes place between the user and the assistant. The user asks a question, and the assistant solves the problem. Please reason step by step in Bengali, and put your final answer in the <answer> </answer> tags.

Question: {problem}"""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=2048, temperature=0.7)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

Using vLLM

vllm serve dipta007/GanitLLM-0.6B_CGRPO --max-model-len 4096

Performance

Related Models

Citation

@inproceedings{dipta2026ganitllm,
  title={GanitLLM: Difficulty-Aware Bengali Mathematical Reasoning through Curriculum-GRPO},
  author={Shubhashis Roy Dipta and Khairul Mahbub and Nadia Najjar},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2026},
  year={2026},
  eprint={2601.06767},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2601.06767},
}

License

This model is released under the Apache 2.0 License.