The Belief State Transformer
Edward S. Hu Kwangjun Ahn Qinghua Liu Haoran Xu Manan Tomar Ada Langford Dinesh Jayaraman Alex Lamb John Langford
We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the prefix and the previous token for the suffix. The Belief State Transformer effectively learns to solve challenging problems that conventional forward-only transformers struggle with, in a domain-independent fashion.  Key to this success is learning a compact belief state that captures all relevant information necessary for accurate predictions.
Empirical ablations show that each component of the model is essential in difficult scenarios where standard Transformers fall short. 
For the task of story writing with known prefixes and suffixes, our approach outperforms the Fill-in-the-Middle method for reaching known goals and demonstrates improved performance even when the goals are unknown. Altogether, the Belief State Transformer enables more efficient goal-conditioned decoding, better test-time inference, and high-quality text representations on small scale problems. Website: https://sites.google.com/view/belief-state-transformer/
representation learning, transformers, next-token prediction, reasoning, planning
We show that transformers trained with belief state representations solve tasks that conventional next-token predictors (e.g. GPT) cannot.