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Found 6 papers, 3 papers with code
Teaching Large Language Models to Reason with Reinforcement Learning
Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint.
Understanding the Effects of RLHF on LLM Generalisation and Diversity
OOD generalisation is crucial given the wide range of real-world scenarios in which these models are being used, while output diversity refers to the model's ability to generate varied outputs and is important for a variety of use cases.
Neurons in Large Language Models: Dead, N-gram, Positional
Specifically, we focus on the OPT family of models ranging from 125m to 66b parameters and rely only on whether an FFN neuron is activated or not.
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
PEER: A Collaborative Language Model
Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes.
On time series representations for multi-label NILM
Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating.
