arXiv:2607.07423v1 Announce Type: new Abstract: We prove that, in the realizable PAC setting, the sample complexity of exact-trace learning for full autoregressive Chain-of-Thought traces is upper bounded by the standard multiclass rate of the local next-token class, where this rate is governed by the Daniely--Shalev-Shwartz dimension. Under exact-trace loss, one wrong action makes the whole trace incorrect; nevertheless, for every stopping rule $\mathtt{halt}$ and every pointwise $\mathtt{halt}$-halting local class $\mathrm{H}$, $n_{\mathrm{PAC}}^{\varepsilon,\delta}(\operatorname{Roll}_{\mat
This research provides fundamental theoretical understanding for the efficient learning of complex AI reasoning processes like Chain-of-Thought, crucial as large language models become more sophisticated.
A strategic reader should care because improved theoretical guarantees for CoT learning directly impact the efficiency, reliability, and scalability of advanced AI agents and their underlying models.
This work potentially changes how AI models learn complex, multi-step reasoning by showing optimal sample complexity, which could lead to more data-efficient training and robust performance.
- · AI researchers
- · Large language model developers
- · Companies deploying AI agents
- · Developers of data-efficient AI systems
- · AI models requiring extensive, unoptimized training data
- · Systems with high error rates in multi-step reasoning
More efficient training of advanced AI reasoning capabilities.
Accelerated development and broader deployment of reliable AI agents across various domains.
Increased competition and innovation in the AI agent space due to lower barriers to effective model development.
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