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ARXIV:2603.08659 · ADAPTIVE REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08659ADAPTIVE REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution.
Opportunity summary
Pain CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution. However, it often falls into another trap:…
The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks.
Adaptive Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution.
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Paper Pack
10.48550/arXiv.2603.08659CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution.
Abstract
The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a disproportionately high cost. This motivates adaptive reasoning: dynamically aligning reasoning depth with instance difficulty. In this paper, we study adaptive reasoning from an optimality perspective, formalizing it as a utility maximization problem where tokens are allocated until the marginal accuracy gain falls below the incremental cost. Based on this, we propose CODA (Compute Allocation by Difficulty Awareness), a method that operationalizes this principle by allocating tokens via a policy-internal difficulty signal. Specifically, CODA estimates difficulty via group-based rollouts and maps it to two non-negative gates that modulate a length-dependent shaping term on top of the binary base reward. The easy-side gate penalizes verbosity on simple instances, whereas the hard-side gate encourages more deliberative rollouts on challenging ones. Across model scales and benchmarks, CODA achieves adaptive reasoning without external annotations or user-provided budgets: on easy tasks, CODA reduces token costs by over 60% while maintaining strong accuracy, whereas on hard tasks it incentivizes more deliberative rollouts to maximize performance.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Dimensions overall score 7.0
PROBLEM
CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution. However, it often falls into another trap: overthinking simple problems, where repetiti...
METHOD
The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a di...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks.
WHY NOW
Adaptive Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a disproportionately high cost.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a disproportionately high cost.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Adaptive Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
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CODA dynamically adjusts reasoning depth based on problem difficulty, reducing compute costs on easy tasks and improving accuracy on hard tasks, making it a cost-effective reasoning solution.
Segment
Adaptive Reasoning
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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reason
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proof status
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Technical feasibility
partial
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