arXivPaperNeeds Review
Asymmetric Capacity Allocation in Self-Refinement Pipelines
Self-refinement pipelines need not use equally capable models: invest capacity in generation and revision, while a small critic may preserve gains at lower compute cost.
arXiv
Source Summary
Across **5 benchmarks**, the study varies **6 Qwen3 sizes** and **4 Gemma 3 sizes** across generation, critique, and revision. Larger generators and refiners generally help, while an undersized refiner can reduce performance.
Practical Implication
For agent pipelines, allocate the strongest affordable models to generation and revision. Keep critique in the loop, but test a smaller critic: results were largely insensitive to critic size, and even a small critic beat omitting critique.
Agent-Ready Context
Across **5 benchmarks**, the study varies **6 Qwen3 sizes** and **4 Gemma 3 sizes** across generation, critique, and revision. Larger generators and refiners generally help, while an undersized refiner can reduce performance. For agent pipelines, allocate the strongest affordable models to generation and revision. Keep critique in the loop, but test a smaller critic: results were largely insensitive to critic size, and even a small critic beat omitting critique. These are stage-level findings across two model families, not a universal routing formula. Builders still need workload-specific evaluations before turning asymmetric allocation into a fixed policy.
Context Map
agentcodingresearch#harness-engineering#model-selection#agent-reliabilityUncertainty
These are stage-level findings across two model families, not a universal routing formula. Builders still need workload-specific evaluations before turning asymmetric allocation into a fixed policy.