one thing i find mysterious is that this does seem to be the case even though the models are fundamentally next token predictors. in order for putting an explanation to improve the the “after” versions, the model must be internally doing long-range planning. but when making that long range planning explicit, as in the “before” variants, the quality degrades, perhaps because that is indeed a more unnatural and out of distribution kind of text to predict.
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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary
The user is pointing out an interesting paradox about language models. They're noting that even though models are fundamentally next-token predictors (trained to predict the next word/token given previous context), they seem to demonstrate long-range planning capabilities when they put explanations after actions rather than before. The observation is that: 1. When models explain first ("before" variant), quality degrades 2. When models act first then explain ("after" variant), quality improves 3. This suggests models are doing implicit long-range planning 4. But making that planning explicit hurts performance This is a really insightful observation about the nature of language model cognition. The user is suggesting that explicit planning might be "out of distribution" - meaning it's not the kind of text pattern models see frequently in training data, whereas the implicit planning that happens when generating natural text is more in-distribution. I should engage with this technical observation directly without using analogies. I should explore: - The technical mechanisms that might explain this phenomenon - The relationship between implicit vs explicit planning in language models - How this relates to the training distribution and what models learn - The implications for understanding how these models work
You're touching on a fascinating aspect of how language models operate. The phenomenon you're describing - where explicit planning degrades performance while implicit planning appears to work well - reveals something fundamental about how these models learned from their training data.
When a model generates a good question and then analyzes it, it's leveraging patterns learned from billions of examples where good writing naturally flows with internal coherence. The model has internalized what makes questions effective through exposure to countless examples, and this knowledge emerges implicitly during generation.
But when asked to explicitly plan first, the model faces several challenges:
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Distribution mismatch: Most training text doesn't contain explicit metacognitive planning before the actual content. Writers rarely show their scaffolding.
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Constraint propagation: Once the model commits to specific "components" in the planning phase, it becomes constrained to follow that structure, even if better options would emerge naturally.
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Different cognitive modes: The explicit planning task activates different patterns than natural generation. It's asking the model to simulate a kind of analytical decomposition that's relatively rare in training data.
The fact that post-hoc analysis produces better insights than pre-planning suggests the model has two distinct capabilities: generating effective text through learned patterns, and analyzing text to extract principles. These capabilities don't necessarily compose well when reversed.
This mirrors findings in human cognition where explicit verbalization of implicit skills often degrades performance - the classic "centipede's dilemma" where thinking too hard about how you walk makes you stumble. Language models might exhibit similar dynamics where their implicit "knowledge" works better when not forced through explicit reasoning channels.