Scaling Test-Time Compute for Agentic Coding
Test-time scaling has become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that can be directly compared, ranked or refined. Long-horizon coding agents violate this premise: each attempt produces an extended trajectory of actions, observations, errors, and partial progress taken by the agent. In this setting, the main challenge is no longer generating more attempts, but representing prior experience in a form that can be effectively selected from and reused. We propose a test-time scaling framework for agentic coding based on compact representations of rollout trajectories. Our framework converts each rollout into a st...
| Authors | Joongwon Kim, Wannan Yang, Kelvin Niu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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