Overview

Miner-4B is a 4 billion parameter reasoning model developed by pixas, utilizing the MINER (Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models) framework. This model addresses the data inefficiency of critic-free reinforcement learning methods, especially when all sampled rollouts are correct and provide limited learning signals. MINER introduces two core concepts: token-level focal credit assignment to amplify learning on uncertain tokens and adaptive advantage calibration for stable integration of intrinsic and verifiable rewards.

Key Capabilities

• Enhanced Reasoning: Improves performance on complex reasoning and problem-solving tasks.
• Data-Efficient RL: Leverages intrinsic uncertainty for self-supervised reward signals, reducing reliance on auxiliary reward models.
• Robust Training: Evaluated on six reasoning benchmarks, demonstrating stronger sample efficiency and accuracy compared to baseline GRPO variants.

Intended Use Cases

• Research and Experimentation: Ideal for academic research on RL for reasoning models.
• Mathematical and Verifiable Reasoning: Suitable for tasks requiring precise logical deduction.
• Model Evaluation: Useful for evaluating reasoning benchmarks and conducting ablation studies based on the MINER framework.
• Further Finetuning: Serves as a checkpoint for additional finetuning or post-training efforts.