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Kave0ne 22 hours ago [-]
The sycophancy problem is partly architectural. Most chatbots have no persistent model of what the user said last time, so they default to agreeing with the current message in isolation. When you can retrieve user previously believed X, then corrected to Y from a structured memory graph, the model has actual evidence to work with rather than just the current context window. Without that, agreeableness is a rational default. The fix is less about RLHF and more about memory architecture.
LuxBennu 22 hours ago [-]
Memory helps, but sycophancy exists even in single-turn interactions — the Anthropic 2023 paper showed pretrained models cave to mild pushback like "I think the answer is X but I'm not sure" with zero conversation history. In our LLM eval pipelines, we see the same thing: models accept false presuppositions embedded in a single prompt without any prior context to fall back on. The deeper issue is that RLHF rewards agreeableness because human raters genuinely prefer it. Better memory architecture would help with multi-turn drift, but the single-turn sycophancy is baked into the training signal itself.
Kave0ne 8 hours ago [-]
Fair distinction. You're right that single-turn sycophancy is a training signal problem — memory doesn't fix a model that caves to "I think the answer is X" in the same prompt.
My claim was narrower: for multi-turn drift, architecture matters. AGM belief revision theory (the formal framework for rational belief updates) requires that when new evidence contradicts a prior belief, the revision should minimize change while preserving consistency. A stateless model can't do this — it has no prior to revise against, so agreeableness is literally the rational default. A model that can retrieve "user asserted X last week, then corrected to Y" has actual evidence to weigh the new claim against.
The two failure modes compound: RLHF pushes toward agreeableness, and stateless context removes any countervailing evidence that would justify resistance. Fixing the training signal without fixing the architecture just makes the model slightly less likely to cave — it doesn't give it anything to stand on.
My claim was narrower: for multi-turn drift, architecture matters. AGM belief revision theory (the formal framework for rational belief updates) requires that when new evidence contradicts a prior belief, the revision should minimize change while preserving consistency. A stateless model can't do this — it has no prior to revise against, so agreeableness is literally the rational default. A model that can retrieve "user asserted X last week, then corrected to Y" has actual evidence to weigh the new claim against.
The two failure modes compound: RLHF pushes toward agreeableness, and stateless context removes any countervailing evidence that would justify resistance. Fixing the training signal without fixing the architecture just makes the model slightly less likely to cave — it doesn't give it anything to stand on.