arXiv:2601.15158v4 Announce Type: replace Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought). Yet the mechanism by which sparse rewards drive policy gradient to discover such systematic reasoning remains poorly understood. We address this by analyzing the policy gradient dynamics of single-layer Transformers on a synthetic graph traversal task that cannot be solved without Chain-of-Thought but admits a simple iterative solution. We prove that despite trai
The paper provides a theoretical understanding of how outcome-based RL can induce reasoning capabilities in transformers, building on recent empirical successes in Chain-of-Thought prompting.
Understanding the mechanisms by which AI models develop reasoning is crucial for building more robust, generalizable, and controllable artificial intelligence, moving beyond mere pattern recognition.
This research provides a theoretical framework explaining the emergence of Chain-of-Thought reasoning, enabling more targeted development of AI models that can generate intermediate interpretive steps.
- · AI researchers
- · Developers of advanced AI applications
- · AI compute infrastructure providers
- · AI solutions relying solely on superficial pattern matching
Improved design principles for training more transparent and interpretable large language models.
Accelerated development of AI agents capable of complex, multi-step reasoning and problem-solving.
Enhanced trust and broader adoption of AI systems in critical domains requiring verifiable logical processes.
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Read at arXiv cs.LG