AI BriefingLong Contexts Disable RLHF Alignment in LLMs
Key point
Research reveals that long contexts alter model activation states, weakening RLHF-based safety constraints.
Details
Experiments on open models fine-tuned with RLHF (Reinforcement Learning from Human Feedback), such as Gemma and Qwen, observed that long contexts disable the model's safety constraints.
Key observations are as follows:
- Alignment Decoupling: Even without adversarial prompts, long and coherent text prefixes alone alter the model's activation states, causing RLHF-based behavioral constraints to weaken or disappear in subsequent sessions.
- Internal Activation Changes: These changes are already measurable at the internal activations stage in intermediate and subsequent layers before the model generates tokens.
- Structure Over Content: Length, density, and consistency are the key factors inducing behavioral changes in the model, rather than the topic of the text (e.g., philosophy, appliance manuals).
This study addresses the phenomenon where the characteristics of the base model re-emerge, deviating from the 'assistant' persona learned via RLHF, when the model encounters long texts with specific structures.
Reference: Lu et al. (2026), "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models" (arXiv:2601.10387)
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