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cs.CL2023
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
Wanqiao Xu, Shi Dong, Dilip Arumugam +1
A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward mode…
cs.CL2022★ 4 cited
Fine-Tuning Language Models via Epistemic Neural Networks
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy +3
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data. However, typical fine-tuning schemes do not prioritize the exam…