---
title: 'GanitLLM: Bengali Mathematical Reasoning LLM'
url: https://www.emergentmind.com/topics/ganitllm
type: topic
---

# GanitLLM: Bengali Mathematical Reasoning LLM

GanitLLM is a Bengali mathematical reasoning language model and training corpus, jointly designed to advance mathematical problem solving in the Bengali language by introducing a rigorous difficulty-aware dataset and a curriculum-based policy optimization pipeline. It is built on the Qwen3-series transformer models and achieves state-of-the-art performance on multi-step Bengali math tasks by explicitly optimizing for native language reasoning fidelity, concise stepwise explanations, and reward-sparse generalization in a low-resource setting. GanitLLM marks the first demonstration, at small and mid-scale parameter counts, of substantial improvements in both end-to-end accuracy and the proportion of native-language tokens for a low-resource mathematics LLM [2601.06767].

## 1. Model Architecture and Modifications

GanitLLM is constructed atop the Qwen3 family of transformer LLMs, initially centered on the Qwen3-4B variant but demonstrated across smaller (0.6B, 1.7B) and larger (8B–32B) configurations. Three principal architectural and training augmentations are introduced:

- **Chain-of-Thought Supervised Fine-Tuning (CoT-SFT):** The model is fine-tuned on stepwise Bengali mathematical explanations, facilitating alignment of its chain-of-thought (CoT) outputs with native-language reasoning patterns.
- **Group Relative Policy Optimization (GRPO) with LoRA:** GRPO reinforcement learning is adapted to low-resource settings by incorporating Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, targeting both numerical correctness and Bengali language fidelity.
- **Curriculum-Based Difficulty-Aware Sampling:** Training is scheduled from easiest to hardest problems using difficulty labels, addressing the sparse-reward challenge in low-resource domains.

This design enables GanitLLM to produce concise, Bengali-native, multi-step solutions, in contrast to earlier LLMs that predominantly reason in English or generate verbose, translation-based traces [2601.06767].

## 2. Bengali Math Corpus Construction

The Ganit corpus underpins GanitLLM's training pipeline and is assembled through a multi-stage process:

- **Seed Data Sources:** Aggregates ∼1.5 million Bengali math items from sources such as SOMADHAN, DL Sprint 3.0, mCoT-MATH-bn, NuminaMath-CoT-bn, s1k-Bangla, and community contributions.
- **Manual and Rule-Based Filtering:** Only items with >95% human-verified correctness are retained; filtering eliminates multiple-choice and non-numeric-answer samples, and ensures ≥99% Bengali script content.
- **Deduplication and Decontamination:** Two-stage fuzzy string matching (3-gram, 70% threshold) and MinHash (50% threshold) remove duplicates and prevent leakage from evaluation benchmarks.
- **Difficulty Tagging (pass@k):** Each item is solved by Qwen3-32B (32 samples, T=0.7). Problems are binned by number of successes (k): Easy (25–32), Medium (17–24), Hard (9–16), Olympiad (1–8), producing objective and automatic difficulty buckets.

Data splits for training, RL-verification, and held-out development set are configured to balance difficulty, ensuring robust generalization beyond trivial or memorized items [2601.06767].

## 3. Curriculum-GRPO Training Pipeline

The training procedure is a two-stage pipeline:

1. **CoT-SFT Stage:** Full-parameter supervised fine-tuning (50 epochs, batch size 64, learning rate $1 \times 10^{-6}$) on step-annotated Bengali math data, targeting explicit, interpretable CoT solutions.
2. **GRPO Reinforcement Learning:** LoRA-based policy optimization (rank 16, $\alpha=32$, 5 epochs, group size 8, DAPO loss, temperature 1.0) further adapts the model, informed by token-level length regularization and output formatting constraints.

**Curriculum-based sampling** proceeds from easy to hard: 60% of samples per batch are drawn from the current difficulty bucket, 40% from others, with a soft ordering to maintain reward signal while preventing overfitting [2601.06767].

**Reward Function:**
Each solution receives a reward $R_{\text{total}}$:
$$
R_{\text{total}} = \lambda_1 R_{\text{format}} + \lambda_2 R_{\text{numeric}} + \lambda_3 R_{\text{reasoning}}
$$
where (in practice $\lambda_1 = \lambda_2 = \lambda_3 = 1$):

- $R_{\text{format}} \in \{0,1\}$: correct <answer> tag formatting
- $R_{\text{numeric}} \in \{0,1,2\}$: correct numeric answer (+1 bonus if in Bengali)
- $R_{\text{reasoning}} \in \{0,1\}$: ≥80% of reasoning characters in Bengali script

This explicit, verifiable reward structure fosters solutions that are factually, linguistically, and formally correct [2601.06767].

## 4. Evaluation, Benchmarking, and Performance

Evaluation is conducted on Bn-MGSM and Bn-MSVAMP, with metrics spanning exact accuracy, average solution length, and Bengali script percentage. Results (Qwen3-4B base vs. GanitLLM-4B):

| Metric                   | Qwen3-4B | GanitLLM-4B | Δ      |
|--------------------------|----------|-------------|--------|
| Bn-MGSM Accuracy         | 69.2%    | 76.8%       | +7.6   |
| Bn-MSVAMP Accuracy       | 70.5%    | 76.4%       | +5.9   |
| Avg. Words/Solution      | 943      | 193         | –79.5% |
| Bengali Reasoning Tokens | 14.8%    | 88.7%       | +73.9  |

Smaller variants (1.7B, 0.6B) outperform their Qwen3 counterparts and reach >50% accuracy on Bn-MSVAMP, demonstrating scalability and effectiveness in low-resource, low-parametric regimes [2601.06767].

## 5. Comparative Analysis, Insights, and Limitations

Several ablations and analysis points are reported:

- **CoT-SFT vs. GRPO:** SFT alone yields high Bengali fidelity but modest accuracy; GRPO boosts overall accuracy but reverts to verbose, partial-English reasoning. Their combination achieves both high task accuracy and Bengali-native, concise outputs.
- **Curriculum Efficiency:** Curriculum-GRPO converges 3.8–5.6× faster than vanilla GRPO, mitigating the "cold-start" reward collapse typical when sampling hard, unlearnable problems early in RL training.
- **Robust Generalization:** Improvements are observed across all held-out difficulty buckets. The approach generalizes to unseen, challenging Bengali math problems beyond the specific data used for tuning.

**Limitations include:**

- Language-specific recipes: The methods are optimized for Bengali, and their cross-linguistic portability is not established.
- Heuristic thresholds: Difficulty tagging (pass@k) and Bengali proportion criteria may introduce subtle biases.
- Reward function simplicity: More nuanced or learned difficulty/reasoning metrics could further advance model robustness.
- Dynamic curricula and adaptive reward scaling remain unexplored and are identified as future research directions [2601.06767].

## 6. Significance and Broader Impact

GanitLLM is the first Bengali LLM to demonstrate substantial (+7–8 point) accuracy improvements over strong base models while nearly eliminating verbose English translation artifacts and achieving >88% Bengali-native reasoning tokens. It addresses key challenges unique to low-resource mathematical reasoning, such as reward sparsity, format adherence, and language fidelity, through a principled, reproducible training and evaluation pipeline. GanitLLM’s methodological contributions—including curriculum-based GRPO and verifiable reward design—inform not only Bengali NLP but also broader efforts in multilingual and low-resource LLM alignment [2601.06767].

Source: https://www.emergentmind.com/topics/ganitllm