The instruction tuning process is designed to significantly improve the model's usability, interactivity, and safety. This process employs an advanced two-stage combination of techniques:
- First, through improved knowledge distillation, knowledge is extracted from a large-scale instruction-tuned teacher model, enabling the student model (Gemma 3) to learn the teacher's policy distribution.
- Second, the model enters a reinforcement learning fine-tuning stage, employing multiple cutting-edge reward optimization algorithms including BOND, WARM, and WARP.
- These methods work together through diverse reward functions to simultaneously improve the model's capabilities across multiple dimensions, including conversational helpfulness, mathematical reasoning, code generation, complex reasoning, instruction following, and multilingual understanding, while striving to minimize harmful outputs.
- Reward signals come from multiple sources: a weighted average reward model trained on human feedback data, ground-truth results from code execution feedback, and accuracy assessments of mathematical problem-solving.