arXiv:2505.13775v4 Announce Type: replace Abstract: Recent impressive results from large reasoning models have been interpreted as a triumph of Chain of Thought (CoT), and especially of the process of training on CoTs sampled from base LLMs in order to help find new reasoning patterns. While these traces certainly seem to help model performance, it is not clear how they influence it, with some works ascribing semantics to them and others cautioning against relying on them as transparent and faithful proxies of the model's internal computational process. To systematically investigate the role o
This research is emerging as large language models continue to integrate complex reasoning tasks, prompting deeper inquiry into their internal mechanisms.
Understanding the true nature of intermediate tokens in LLMs is critical for advancing AI capabilities and developing more reliable and explainable models.
The interpretation of Chain of Thought reasoning, moving from a semantic-only view to one acknowledging the effectiveness of 'reasonless' tokens, potentially alters model development strategies.
- · AI researchers
- · LLM developers
- · Companies seeking more efficient AI training
- · Those overly relying on semantic interpretation of CoT
- · Skeptics of emergent AI capabilities
More efficient and less computationally expensive methods for training and fine-tuning large reasoning models may emerge.
This could lead to breakthroughs in autonomous AI agents that require robust and interpretable reasoning capabilities.
A deeper theoretical understanding might enable new AI architectures less reliant on brute-force scaling, impacting the compute supply chain.
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Read at arXiv cs.LG