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Sub-goal Distillation: A Method to Improve Small Language Agents
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks.
Offline-Online Reinforcement Learning: Extending Batch and Online RL
To enable better performance, we investigate the offline-online setting: The agent has access to a batch of data to train on but is also allowed to learn during the evaluation phase in an online manner.
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read.
Exploiting generalization in the subspaces for faster model-based learning
Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace.
