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Found 3 papers, 2 papers with code
Learning Natural Language Generation with Truncated Reinforcement Learning
To our knowledge, it is the first approach that successfully learns a language generation policy without pre-training, using only reinforcement learning.
Diffusion bridges vector quantized Variational AutoEncoders
In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.
Learning Natural Language Generation from Scratch
This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement learning (RL).
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