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Found 8 papers, 4 papers with code
Vanishing Gradients in Reinforcement Finetuning of Language Models
Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms.
When can transformers reason with abstract symbols?
We investigate the capabilities of transformer models on relational reasoning tasks.
Transformers learn through gradual rank increase
Our experiments support the theory and also show that phenomenon can occur in practice without the simplifying assumptions.
Position Prediction as an Effective Pretraining Strategy
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec models in Speech and, recently, in MAE models in Vision, forces the model to learn about relationships between the content in different parts of the input using autoencoding related objectives.
The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
While common and easily reproduced in more general settings, the Slingshot Mechanism does not follow from any known optimization theories that we are aware of, and can be easily overlooked without an in depth examination.
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation
Learning generalizeable policies from visual input in the presence of visual distractions is a challenging problem in reinforcement learning.
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
