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Found 4 papers, 1 papers with code
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
Here, we introduce MosaicBERT, a BERT-style encoder architecture and training recipe that is empirically optimized for fast pretraining.
LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms
We find that subsets of 1k-6k instruction finetuning samples are sufficient to achieve good performance on both (1) traditional NLP benchmarks and (2) model-based evaluation.
Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
