Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck
\ac{CoT} prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing ``Budget Forcing'' methods reduce cost via fine-tuning with heuristic length penalties, suppressing both essential reasoning and redundant filler. We recast efficient reasoning as a lossy compression problem under the \ac{IB} principle, and identify a key theoretical gap when applying naive \ac{IB} to transformers: attention violates the Markov property between prompt, reasoning trace, and response. To resolve this issue, we model \ac{CoT} generation under the \ac{CIB} principle, where the reasoning trace $Z$ acts as a computational bridge that contains only the informati...
| Authors | Fabio Valerio Massoli, Andrey Kuzmin, Arash Behboodi |
|---|---|
| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 18 May 2026 |
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