This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct trained on the MATH dataset for one epoch using Intuitor.

Intuitor is a reinforcement learning method introduced in the paper Learning to Reason without External Rewards. It fine-tunes Large Language Models (LLMs) using self-certainty—the model’s own internal confidence—as the sole reward signal.

This approach is part of a novel framework called Reinforcement Learning from Internal Feedback (RLIF), which enables models to learn from intrinsic signals without the need for external rewards, gold labels, or test-case verifiers. Intuitor replaces external rewards in Group Relative Policy Optimization (GRPO) with self-certainty scores, enabling fully unsupervised learning that matches or exceeds standard RL performance on mathematical and coding benchmarks.