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Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement

NeurIPS 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Reinforcement learning for visual reasoning

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Studies RL-induced chain-of-thought obfuscation in VLMs via attention, integrated gradients and frozen-SAE template features, then proposes TAME, which combines an anti-obfuscation prompt-evolution feedback loop with an asymmetric SAE penalty on template activations to improve CoT monitorability on VIRL-39k and SPA-VL.

Problem

GRPO-style RL on vision-language models raises task reward while CoT traces become ungrounded and hard to monitor (CoT obfuscation), and the representational correlates of this decline are unclear.

Approach

Localizes RL-induced CoT obfuscation to template-associated SAE features in specific layers, validates them with matched targeted vs random interventions, and penalizes template activations only above their pre-RL baseline during training, combined with a behavioral prompt-evolution feedback loop.

Key results

• TAME raises Qwen3-VL-8B G-mean monitorability on VIRL from 0.302 (GRPO) to 0.611, and on SPA-VL from 0.394 to 0.561.
• Human blinded study: human monitorability preference favors TAME by 12.50 pp (VIRL) and 13.75 pp (SPA-VL) over GRPO.
• Task accuracy on VIRL changes only from 0.646 (GRPO) to 0.640 (TAME) for Qwen; general-capability results show task-specific trade-offs, including MMMU-Pro drop from 48.6 to 43.0 on VIRL-trained Qwen.

Surprising findings

• Injection of template features at step 0 degrades monitorability only modestly, so amplifying the same directions in an untrained model leaves most of the trained-model decline unexplained.
• Task accuracy changes under TAME are small and mixed, and DAPO remains comparable to GRPO on monitorability.

Limitations

• Causal contribution of the localized features is partial and dataset-specific, per the authors.
• Monitorability is measured with GPT-4o and reference labels built from LLM judges (DeepSeek-V3.2, Gemini-2.5-Flash), with a small human study (80 units per dataset).
• General-capability trade-offs depend on model and dataset, with VIRL-trained Qwen showing a notable MMMU-Pro regression.

Experimental setup

Datasets: GQA VIRL-39k SPA-VL

Benchmarks: VIRL-39k held-out SPA-VL test-harm SPA-VL test-help MMMU-Pro MathVista MathVerse MEGA Core

Metrics: G-mean monitorability (TPR x TNR) monitor accuracy task accuracy Harm Score Win Rate human monitorability preference

Baselines: GRPO DAPO CoT-monitor reward SAE-only Behav-only Behav + Rand-SAE

Builds on: GRPO DAPO experiential RL TopK sparse autoencoder

Closest papers by meaning

• On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs (ICML 2026 · similarity 0.90)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
• Detecting RLVR Training Data via Structural Convergence of Reasoning (NeurIPS 2026 · similarity 0.88)
• NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation (NeurIPS 2025 · similarity 0.88)
• Activating Visual Context and Commonsense Reasoning Through Masked Prediction in VLMs (AAAI 2026 · similarity 0.88)
• ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs (NeurIPS 2025 · similarity 0.88)