Larger AI Models Complete More Steps in Long-Horizon Execution Tests

Larger models completed more turns correctly than smaller models, even when smaller models had near-perfect single-turn accuracy. Researchers supplied the knowledge and plan needed for each assigned task, then measured whether models carried out the steps. Per-step accuracy declined as tasks gained steps. Self-conditioning occurred when a model became more likely to make further errors after its conversation history included its own earlier mistakes. Model size alone did not eliminate the effect. Thinking mitigated self-conditioning and enabled models to complete longer tasks in a single turn. Simon Yoon reports on blockchain technology for TokenPost. Send corrections or tips to info@tokenpost.com.

Source: TokenPost