Kaggle Competition Practice: Comprehensive Analysis of NVIDIA Nemotron Model Reasoning Capability Optimization

This article focuses on the practical solutions for the Kaggle NVIDIA Nemotron Model Reasoning Challenge, covering LoRA fine-tuning, CoT data synthesis, SFT and DPO training strategies, as well as key experiences and pitfall avoidance guidelines summarized by the team. The competition goal is to improve the performance of the Nemotron-3-Nano-30B-A3B model on multi-dimensional reasoning tasks, and this article systematically introduces the complete technical path from baseline reproduction to advanced optimization.

The core challenge of this competition is to improve the reasoning quality of a 30-billion parameter MoE model. Nemotron-3-Nano-30B-A3B uses a mixture-of-experts architecture, activating only about 3 billion parameters per forward pass to balance performance and computational cost. The tasks cover dimensions such as bit operations, equation transformation, gravitational constant calculation, base conversion, text encryption, unit conversion, etc. The evaluation metric is pass@5: generate 5 answers per question, get 0.2 points for one correct answer, encouraging diverse reasoning paths.

The original training set has 6558 samples, and after filtering, 2907 are retained (quality over quantity). CoT synthesis process: 1. Generate diverse reasoning chains; 2. Verify answer correctness via programs/rules; 3. Deduplicate to maintain diversity; 4. Quality filtering (prioritize complete and concise chains); 5. Segment training (separate reasoning process and answer to avoid excessive focus on form).

LoRA Configuration: Use PEFT library, Rank=32, Alpha=16, target modules are in_proj/out_proj/up_proj/down_proj, Dropout=0.05, task type CAUSAL_LM. Training Strategy: SFT (Supervised Fine-Tuning, reproduce baseline 0.64 score) → DPO (Preference Alignment) → GRPO (Reasoning Stability Optimization) → TTS (Test-Time Scaling such as BoN/ToT).

1. Trust CoT only after verifying the answer: Must validate answer correctness to avoid being misled by fluent but wrong reasoning chains; 2. Teacher model quality determines the upper limit: Stronger teacher models yield higher distillation benefits; 3. Prioritize sample verifiability: Use automated methods to check answers (programs/solvers, etc.); 4. Prevent overfitting: Mix synthetic and real data for training, monitor validation set; 5. Control output length: Limit to within 8K to avoid redundancy.

Baseline Scheme Comparison: The baseline schemes of jal313 and Zhang Wuji scored 0.64, while konbu17 reached ~0.70 via fine CoT filtering. Project Structure: The repository includes 70.0-upgrade, data, scripts, tests, artifacts (including LoRA adapters), submission sample Notebooks, etc. Quick Start: Install dependencies → Place train.csv → Execute Notebook steps.

1. Design multiple sets of Prompts: Try different templates during testing to stimulate different reasoning modes; 2. Difficulty-level training: Design differentiated strategies for easy/medium/hard levels; 3. Record reasoning chains: Facilitate subsequent analysis and model iteration; 4. Dual evaluation mechanism: Local rapid iteration + official submission to verify real effects.