Emerging in 2026

Is test-time compute better than fine-tuning for improving quality?

It depends on the failure mode. Test-time compute (longer reasoning, self-consistency, verifier passes) helps on tasks the model *can* solve but often fails on — reasoning, math, code. Fine-tuning helps on tasks the model consistently fails at because it lacks the pattern — domain-specific writing style, structured extraction on your schema, tool-use patterns. In 2026 the default is: reach for test-time compute first (no data collection, no retraining), fall back to fine-tuning when you have >1k high-quality examples and consistent failure signatures.

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For developers

Call this framework and its tools from your own agent via the Model Context Protocol (MCP) server. Works with Claude Desktop, Cursor, Zed, Continue, and the OpenAI Agents SDK.

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