How to Leverage Inference-Time Compute in Competitions
dejavucoder · x · 2026-07-15
This post shares practical experience on using **inference-time compute** during competitions: - **Start as early as possible**: Due to certain reasons, the author began using inference-time compute mid-competition, quickly exhausted their tokens, and had to rely on quota resets. - **The feedback loop between the model and the environment is crucial**: The model's "breakthroughs" stem partly from continuous trial and error, rejecting bad ideas, and iterating based on feedback from the agent and the environment. - **Model comparisons**: The author feels `gpt-5.6-sol` is very capable but burns through quotas too fast. `gpt-5.6-pro` acts more like an excellent advisor, performing even better than `fable-5` on this specific problem. - **Automation potential**: For the first problem, about 30% required human intervention (HITL), while the model ran on its own about 40% of the time. If `gpt-5.6-pro` could also be integrated into the harness, full automation might be achievable.
Related event: Practical Tips for Using Inference-Time Compute in Competitions(2 posts)→
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