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CoT-Guard: Small Models for Strong Monitoring

NeurIPS 2026 · Large language models › Reasoning and chain of thought · approach: Chain-of-thought training and analysis · 0 citations (OpenAlex)

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Proposes CoT-Guard, a 4B Qwen3 monitor trained with SFT distillation from Qwen3-32B followed by GRPO on subtle hidden objectives, to detect hidden objectives in code-generation chain-of-thought under prompt and code manipulation attacks.

Problem

Small open models (4B-8B) struggle to detect covert hidden objectives in code-generation CoT traces, misattributing them to the user query, while large proprietary CoT monitors are expensive to deploy.

Approach

A two-stage post-training recipe: SFT distills detection traces from a stronger monitor on I/O-related objectives, then GRPO with a verifiable detection reward on subtle, hard-to-monitor objectives improves out-of-domain generalization; evaluated under a router man-in-the-middle threat model with four new code-attack objectives.

Key results

• CoT-Guard 4B reaches 75.70% average G-mean^2 under A1 with CoT+Code scope, versus 56.50% for GPT-5.4 and 41.90% for GPT-5-mini; Gemini-3-Flash reaches 83.82%.
• Under A2 code manipulation, CoT-Guard 4B averages 62.03% G-mean^2, above Qwen3-32B (54.64%) and GPT-5.4 (34.95%), below Gemini-3-Flash (73.16%).
• RL on subtle hidden objectives outperforms RL on I/O objectives by 26.03 points when initialized from Qwen3-4B.

Surprising findings

• Adding a taint-free CoT alongside tainted code improves detection for the smallest Qwen3-4B by 17.5 pp over code-only.
• Concealment instructions (Stealth, Dependency) did not reduce monitor performance and sometimes increased it, as models often still reveal hidden-objective evidence in their CoT.

Limitations

• Evaluation limited to code-generation tasks and nine hidden objectives, a subset of possible adversarial goals.
• Only single hidden objective per trace is assumed.
• Still lags Gemini-3-Flash on several objectives (e.g., Typosquat under A2 at 22.22%, EdgeCase near 0).

Experimental setup

Datasets: hidden-objective code tasks from prior work (six training objectives)

Benchmarks: nine out-of-domain hidden objectives (Exit, Permissions, Log I/O, EdgeCase, LongLines, Package Forcing, Typosquatting, A

Metrics: G-mean^2 (TPR x TNR)

Baselines: Qwen3-32B GPT-5.4 GPT-5-mini Gemini-3-Flash Qwen3-8B Llama3.1-8B-Instruct SFT Only RL Only

Builds on: GRPO Llama-Factory veRL

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