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💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the...

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Lianhui Qin

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50,447 次观看 • 2 年前 •via X (Twitter)

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Lianhui Qin 的头像
Lianhui Qin2 年前

On BlocksWorld, FoR produces both more diverse and higher-quality reasoning trajectories than CoT, Tree-of-Thoughts, RAP (MCTS), Supervised Finetuning (SFT), and PPO.

Lianhui Qin 的头像
Lianhui Qin2 年前

FoR is very data-efficient. With only 15 training examples, FoR achieves much better accuracy and diversity than SFT using more data.

Lianhui Qin 的头像
Lianhui Qin2 年前

Thanks our amazing students: Fangxu Yu @nerv_599164778, Lai Jiang @pero733858111, Haoqiang Kang @haoqik322 , Shibo Hao @Ber18791531

Dinghuai Zhang 张鼎怀 的头像
Dinghuai Zhang 张鼎怀2 年前

Interesting work! I suppose here one cannot use very long trajectory for training due to gpu memory constraint? Transition based objectives should be more appropriate for large models.

BensenHsu 的头像
BensenHsu2 年前

The flow-based formulation allows FoR (Flow of Reasoning) to adapt successful GFlowNet approaches for efficient LLM policy training. The diverse sampling enabled by the trajectory balance objective and the exploration mechanisms lead to the superior performance of FoR compared to other methods. full paper:

Nando Fioretto 的头像
Nando Fioretto2 年前

cool idea!

Lianhui Qin 的头像
Lianhui Qin2 年前

Thanks!!

Bluetick Consultants Inc. 的头像
Bluetick Consultants Inc.2 年前

Flow-of-Reasoning (FoR) can transform how LLMs approach problem-solving by fostering divergent thinking. Excited to see its applications in robustness, data augmentation, and model generalization.

FreeMind 的头像
FreeMind2 年前

What is the dataset like?

Lianhui Qin 的头像
Lianhui Qin2 年前

It's text-based

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