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Found 5 papers, 2 papers with code
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models.
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost.
$λ$-models: Effective Decision-Aware Reinforcement Learning with Latent Models
The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning.
Pseudo-Inverted Bottleneck Convolution for DARTS Search Space
Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2.
Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm
This technique uses the permutation matrix of QR for feature selection which is a unique property to this factorization method.
