Outcome Supervision

Outcome supervision in large language models (LLMs) focuses on training models to produce correct final outputs, rather than meticulously supervising each intermediate reasoning step. Current research explores efficient methods for achieving this, including reinforcement learning algorithms and novel approaches like reverse curriculum learning, which leverage correct demonstrations to implicitly guide the model's learning process. This approach offers a significant advantage by reducing the need for extensive, labor-intensive annotation of intermediate steps, making training more scalable and cost-effective. The resulting improvements in reasoning accuracy across diverse tasks, including mathematical problem-solving and event extraction, highlight the potential of outcome supervision to enhance LLM capabilities in various applications.