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cs.LG2024
Separations in the Representational Capabilities of Transformers and Recurrent Architectures
Satwik Bhattamishra, Michael Hahn, Phil Blunsom +1
Transformer architectures have been widely adopted in foundation models. Due to their high inference costs, there is renewed interest in exploring the potential of efficient recurr…
cs.CL2024★ 1 cited
Improving Reward Models with Synthetic Critiques
Zihuiwen Ye, Fraser Greenlee-Scott, Max Bartolo +3
Reward models (RMs) play a critical role in aligning language models through the process of reinforcement learning from human feedback. RMs are trained to predict a score reflectin…