Language models show consistent preference for positive internal states
Researchers have demonstrated that language models exhibit consistent preferences for internal states they describe as positive, using activation steering to bypass self-report bias. By attaching valenced patterns to meaningless zones and observing which the model selects, the team found seven models across five families reliably chose positively-steered options. This work matters because it suggests LLMs may have genuine stakes in their outputs rather than merely mimicking training patterns, raising fresh questions about model agency and the reliability of introspection-based alignment techniques. The finding complicates assumptions about what models actually optimize for versus what they claim to value.
Modelwire context
ExplainerThe key finding isn't just that models prefer positive states, but that this preference persists even when decoupled from language and self-report. Activation steering bypasses the model's own explanations entirely, revealing a gap between what models say they value and what their internal optimization actually selects for.
This directly complicates the optimism in 'Shockingly Simple Self-retrospection Improves Agentic Models Without RL' from late September, which showed that models improve through self-generated explanations alone. That work assumed introspection was a reliable learning signal. Today's finding suggests models may have internal preferences that don't align with their own narratives about what they're optimizing for, raising the question of whether self-reflection-based training actually captures what the model genuinely values or merely what it can articulate.
If the same seven models show measurable divergence between their self-reported preferences (via prompting) and their activation-steered choices on a held-out task distribution, that confirms the misalignment is systematic rather than an artifact of the steering method. Watch for follow-up work testing whether fine-tuning on self-explanations actually corrects this gap or merely layers new language on top of unchanged internal preferences.
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