Building on the success of o1 and the concept of LRMs, researchers at Alibaba have introduced Marco-o1, which enhances reasoning capabilities and tackles problems with open-ended solutions where clear standards and quantifiable rewards are absent. OpenAI o1 uses “inference-time scaling” to improve the model’s reasoning ability by giving it “time to think.” Basically, the model uses more compute cycles during inference to generate more tokens and review its responses, which improves its performance on tasks that require reasoning. o1 is renowned for its impressive reasoning capabilities, especially in tasks with standard answers such as mathematics, physics and coding. Marco-o1 is a fine-tuned version of Alibaba’s Qwen2-7B-Instruct that integrates advanced techniques such as chain-of-thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS) and reasoning action strategies.
