Depth Scaling
A form of test-time scaling that allows a model to generate longer reasoning chains, progressively decomposing a problem, verifying intermediate steps and correcting its derivations. It is suited to tasks involving multi-step deduction.
Timeline (last 90 days)
Depth scaling means that when generating an answer, the model is allowed a longer reasoning chain, gradually decomposing the problem, verifying intermediate steps, and correcting the derivation process, with the advantage of logical coherence.
The limitation of depth scaling is that if the initial reasoning direction is wrong, a deeper reasoning chain will only go further and further down the wrong path.
The three dimensions of depth, width, and feedback share the same reasoning budget pool; increasing depth means reducing opportunities for width exploration, and investing in feedback iterations may sacrifice the depth of a single reasoning pass.
For mathematical proof tasks, depth scaling is usually more effective because the logical chain needs to remain coherent; for open-domain question answering, width scaling has more advantages; for code generation and debugging, feedback mechanisms are most valuable because compilers and test cases provide reliable external verification signals.
All Facts (4)
Depth scaling means that when generating an answer, the model is allowed a longer reasoning chain, gradually decomposing the problem, verifying intermediate steps, and correcting the derivation process, with the advantage of logical coherence.
50%UnverifiedThe limitation of depth scaling is that if the initial reasoning direction is wrong, a deeper reasoning chain will only go further and further down the wrong path.
50%UnverifiedThe three dimensions of depth, width, and feedback share the same reasoning budget pool; increasing depth means reducing opportunities for width exploration, and investing in feedback iterations may sacrifice the depth of a single reasoning pass.
50%UnverifiedFor mathematical proof tasks, depth scaling is usually more effective because the logical chain needs to remain coherent; for open-domain question answering, width scaling has more advantages; for code generation and debugging, feedback mechanisms are most valuable because compilers and test cases provide reliable external verification signals.
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