arXiv:2506.18831v3 Announce Type: replace Abstract: Reasoning LLMs trained with long chain-of-thought often overthink: they spend tokens on redundant reflection and transitions that inflate cost without improving accuracy. Static activation steering (e.g.\ SEAL) suppresses such content with a fixed vector, but applies the same strength regardless of how redundant the current chunk actually is. We describe PID-steering, a training-free, decoding-time method that modulates the steering strength with a PID controller driven by a lightweight chunk-level redundancy classifier. On a subset of GSM8K
The increasing cost and inefficiency of large language models are driving innovation in methods to optimize their performance and reduce operational overhead.
Improving LLM efficiency directly impacts their economic viability, enabling broader deployment and more complex applications without proportional increases in compute resources.
Decoding-time steering methods like PID-steering offer a training-free pathway to more efficient LLM inference, reducing redundant 'overthinking' and lowering costs.
- · LLM developers and operators
- · Cloud compute providers
- · AI application developers
- · Inefficient LLM architectures
- · High-cost LLM service providers
Reduced inference costs for complex LLM reasoning tasks.
Accelerated development and deployment of agentic AI systems due to lower operational expenditures per query.
Increased accessibility and democratization of advanced AI capabilities, potentially leading to new business models and services.
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