Adaptive Activation Steering for Efficient LLM Reasoning via Closed-Loop PID Control
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Computer Science > Computation and Language
Title:Adaptive Activation Steering for Efficient LLM Reasoning via Closed-Loop PID Control
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 with DeepSeek-R1-Distill-Qwen-1.5B, the method improves accuracy from 85.7\% to 89.6\% (+3.9 pp) while cutting average output length from 1026 to 790 tokens ($-$23\%). We report it as a small-scale proof of concept rather than a benchmark result.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2506.18831 [cs.CL] |
| (or arXiv:2506.18831v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.18831
arXiv-issued DOI via DataCite
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Submission history
From: Ram Bharadwaj Aryasomayajula Mr [view email][v1] Mon, 23 Jun 2025 16:47:19 UTC (73 KB)
[v2] Sat, 11 Apr 2026 12:29:43 UTC (69 KB)
[v3] Mon, 15 Jun 2026 20:03:11 UTC (173 KB)
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