Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

We solve a 2D Poisson problem with a 5-point finite-difference stencil and compare Jacobi vs. Gauss–Seidel relaxation. The plots show how the error field u − u_s ...

Abstract: Transfer learning in robotics aims to transfer knowledge across different robot agents or tasks. Current methods in trajectory tracking problems leverage transferred knowledge to provide a ...

An online iterative alignment pipeline that generates on-policy data, scores responses with a reward model, constructs preference pairs, and trains with DPO -- closing the distribution gap of offline ...

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Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Researchers and developers ...