arXiv:2511.17852v2 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve complex reasoning tasks through fine-tuning. Reinforcement learning (RL) and supervised fine-tuning (SFT) are two primary approaches to this end. In this work, we specifically examine RL with process rewards and SFT for learning $k$-sparse Boolean functions with a one-layer transformer through intermediate reasoning steps akin to CoT. In particular, we consider $k$-sparse Boolean functions that can be recursively decomposed into fixed 2-sparse Boolean functions. We first an
The paper was published now as research into fine-tuning transformers via RL and SFT is a current frontier in improving AI's reasoning capabilities, particularly for complex tasks.
This research provides fundamental insights into how different fine-tuning methods impact the learning capabilities of transformers, which is crucial for developing more robust and efficient AI models.
A clearer understanding emerges of the specific learning mechanisms and differences between RL and SFT when applied to transformers for intricate reasoning tasks, potentially leading to optimized training strategies.
- · AI researchers
- · ML model developers
- · Companies deploying advanced AI
- · Developers of AI agentic systems
- · AI development with suboptimal training methods
- · Systems relying on less interpretable AI reasoning processes
Improved fine-tuning methodologies for large language models will lead to more capable AI.
Enhanced AI reasoning could accelerate the development and reliability of autonomous AI agents.
More sophisticated and interpretable AI could drive breakthroughs in scientific discovery and complex problem-solving across various sectors.
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