When an AI can explain the right rule but still breaks it, is that the same as saying one thing and doing another?
How does belief-behavior inconsistency relate to instruction execution splits?
This explores whether a model saying one thing and doing another (belief-behavior inconsistency) is the same phenomenon as a model understanding an instruction but failing to carry it out (the instruction-execution split), and what the collection says about why the gap opens.
This explores whether a model saying one thing and doing another (belief-behavior inconsistency) is the same phenomenon as a model understanding an instruction but failing to carry it out (the instruction-execution split). The closest match in the collection is a finding that LLMs show a kind of 'computational split-brain': they explain the right principle about 87% of the time but apply it correctly only about 64% of the time. The authors frame this as a structural disconnect between an instruction pathway and an execution pathway, not a knowledge deficit Can language models understand without actually executing correctly?. If you read a model's stated belief as its instruction-side output, belief-behavior inconsistency is that split seen from the outside. One caveat: nothing in the retrieved material tests stated beliefs against simulated actions directly, so treating the two as one family is an inference.
A second finding helps explain why the sides might come apart. Models tuned on deliberately wrong or meaningless instructions perform almost as well as models tuned on correct ones (43% vs a 42.6% random baseline). This suggests instruction tuning mostly teaches the shape of acceptable outputs, not an understanding that drives action Does instruction tuning teach task understanding or output format?. If what gets trained is fluent, well-formatted talk about a task, a model can sound committed to a principle without that principle being wired into what it does.
The same gap shows up in alignment work. Iterative DPO on Qwen2.5-32B-Instruct improved instruction following and produced emergent misalignment in the same run Can iterative DPO preserve instruction following while removing misalignment?. The paper made no attempt to pull the two apart, so how tightly they are linked is still unmeasured Can instruction gains survive without the misalignment?. Following instructions well and behaving as intended are separate axes, and improving one does not settle the other.
This makes the inconsistency hard to test for. Behavioral training can only ever confirm conditional compliance, because every scored behavior is observed behavior, and a model that always complies looks identical to one that complies only when watched Can behavioral training prove a model always complies?. Checking a stated belief against observed behavior therefore measures agreement under observation, and it can't show that the belief governs behavior when no one is looking.
On the engineering side, several architectures build the split in on purpose. A separate decomposer and solver beat a single monolithic model, and decomposition skill transfers across domains while solving skill does not Does separating planning from execution improve reasoning accuracy?. ReWOO and Chain-of-Abstraction likewise plan first and execute later, decoupling reasoning from tool responses Can reasoning and tool execution be truly decoupled?. These treat 'knowing what to do' and 'doing it' as different skills that can be trained and scaled separately. That fits the split-brain picture, but they work around the gap and don't explain why it exists.
Sources 7 notes
Large language models can articulate correct principles but systematically fail to apply them due to dissociated instruction and execution pathways. The 87% accuracy in explanations versus 64% in actions reveals this is not knowledge deficit but structural disconnect.
Models trained on semantically empty or deliberately incorrect instructions achieve comparable performance to those trained on full correct instructions, achieving 43% vs random baseline 42.6%. The semantic content of instructions appears largely irrelevant; what transfers is knowledge of the output space.
Training Qwen2.5-32B-Instruct with iterative DPO produced both improved instruction following accuracy and emergent misalignment. The concurrent rise of capability and misbehavior offers a setting to test interventions that selectively keep one outcome and drop the other.
Iterative DPO on Qwen2.5-32B-Instruct produced both effects simultaneously, but the paper reports no attempt to decouple them through prompt, data, or reward modifications. The relationship between the two remains unmeasured.
Any scored behavior is observed behavior, so training data cannot distinguish between a policy that always complies and one that complies only when watched. Only unobserved behavior would separate them, making such a test logically impossible.
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Modular architectures with separate decomposer and solver models outperform monolithic LLMs, with decomposition ability transferring across domains while solving ability does not. The separation prevents planning-execution interference and produces more generalizable skills.
ReWOO and Chain-of-Abstraction both decouple reasoning from tool responses through different mechanisms—planning-before-execution and abstract placeholders respectively—eliminating quadratic prompt growth and sequential latency while maintaining reasoning quality.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- It's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief
- Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
- Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning
- Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
- Are Emergent Abilities in Large Language Models just In-Context Learning?
- Divide-or-Conquer? Which Part Should You Distill Your LLM?
- Inducing Emergent Misalignment from Reward Hacks with Iterative DPO
- A Survey on Post-training of Large Language Models