Competitive Programming with Large Reasoning Models

Generalist vs. specialist: A tailored model (o1-ioi) with hand-crafted strategies for coding competitions achieved decent results (placing ~50th percentile at IOI 2024 with some relaxed competition constraints). However, a larger, general-purpose model (o3) attained gold medal-level performance without any domain-specific tricks.

Reinforcement learning payoff: Both models were improved via RL fine-tuning, but the scaled general model outperformed the expert pipeline, solving programming tasks at a level comparable to elite human coders (even matching top human ratings on Codeforces).

Efficiency through scale: The results suggest that investing compute in a bigger, broadly-trained transformer can yield greater efficiency and performance than building task-specific optimizations. In other words, scaling up a model’s reasoning ability can supersede manual efficiency tweaks for complex tasks.

Implication: For difficult reasoning tasks like coding, a single large model with sufficient training can simplify deployment (no custom inference routines needed) and still beat highly optimized specialist systems, pointing toward a trend of “scale over special-case” in transformer design.