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Found 14 papers, 10 papers with code
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.
Uncovering Latent Human Wellbeing in Language Model Embeddings
Do language models implicitly learn a concept of human wellbeing?
A StrongREJECT for Empty Jailbreaks
We show that our new grading scheme better accords with human judgment of response quality and overall jailbreak effectiveness, especially on the sort of low-quality responses that contribute the most to over-estimation of jailbreak performance on existing benchmarks.
ALMANACS: A Simulatability Benchmark for Language Model Explainability
The ALMANACS scenarios span twelve safety-relevant topics such as ethical reasoning and advanced AI behaviors; they have idiosyncratic premises to invoke model-specific behavior; and they have a train-test distributional shift to encourage faithful explanations.
Image Hijacks: Adversarial Images can Control Generative Models at Runtime
In this work, we focus on the image input to a vision-language model (VLM).
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
And how do we measure these behaviors in general-purpose models such as GPT-4?
imitation: Clean Imitation Learning Implementations
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch.
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
In this work, we show that any locally optimal symmetric strategy profile is also a (global) Nash equilibrium.
An Empirical Investigation of Representation Learning for Imitation
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
RvS: What is Essential for Offline RL via Supervised Learning?
Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL.
The Essential Elements of Offline RL via Supervised Learning
These methods, which we collectively refer to as reinforcement learning via supervised learning (RvS), involve a number of design decisions, such as policy architectures and how the conditioning variable is constructed.
Sparse Graphical Memory for Robust Planning
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons.
A Map Equation with Metadata: Varying the Role of Attributes in Community Detection
In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure.
Post-processing partitions to identify domains of modularity optimization
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics.
Social and Information Networks Physics and Society
