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Found 8 papers, 4 papers with code
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).
When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning
Past analyses of reinforcement learning from human feedback (RLHF) assume that the human fully observes the environment.
STARC: A General Framework For Quantifying Differences Between Reward Functions
This means that reward learning algorithms generally must be evaluated empirically, which is expensive, and that their failure modes are difficult to anticipate in advance.
imitation: Clean Imitation Learning Implementations
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch.
Calculus on MDPs: Potential Shaping as a Gradient
In reinforcement learning, different reward functions can be equivalent in terms of the optimal policies they induce.
Preprocessing Reward Functions for Interpretability
In many real-world applications, the reward function is too complex to be manually specified.
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice
However, we then present a simple new algorithm for seeded segmentation / graph-based semi-supervised learning that is closely based on Karger's original algorithm, showing that for these problems, extensions of Karger's algorithm can be useful.
Steerable Partial Differential Operators for Equivariant Neural Networks
In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential operators (PDOs) in physics.
