Evaluating Self-Improving Coding Agents with SIFT: Cost, Accuracy, and Reward Hacking

MIT and Sakana AI's SIFT framework uses an LLM judge to reduce evaluation costs for self-improving coding agents, balancing judge accuracy, compute overhead, and reward hacking risks.