arXiv:2606.13125v1 Announce Type: cross Abstract: Reinforcement learning has rapidly emerged as a key component in the training of reasoning and coding models, yet it remains poorly understood from a mechanistic perspective. We study how and through what underlying processes capabilities are acquired or enhanced via reinforcement learning post-training. Our analysis, based on controlled math reasoning experiments with Qwen-2.5-1.5B, reveals two core mechanisms: strategy selection and strategy improvement. Our results highlight the role of SFT data and reinforcement learning data in activating
This research provides timely insights into the foundational mechanics of reinforcement learning in AI, which is currently a rapidly evolving and critical component of advanced model development.
Understanding how AI models acquire and enhance reasoning capabilities through post-training is crucial for developing more robust, reliable, and capable AI systems, impacting future applications across various sectors.
The mechanistic understanding of strategy selection and improvement in post-training offers a clearer pathway for optimizing reinforcement learning, potentially leading to faster and more efficient AI development cycles.
- · AI researchers
- · AI model developers
- · Companies investing in advanced AI
- · AI development relying solely on heuristic methods
Improved efficiency and performance in AI model training, particularly for reasoning and coding tasks.
Accelerated development of more sophisticated and autonomous AI agents capable of complex problem-solving.
Enhanced AI capabilities could redefine human-computer interaction and automation across scientific and industrial domains.
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Read at arXiv cs.AI