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Found 13 papers, 7 papers with code
OpenAI o1 System Card
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.
GPT-4o System Card
We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
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A StrongREJECT for Empty Jailbreaks
To create a benchmark, researchers must choose a dataset of forbidden prompts to which a victim model will respond, along with an evaluation method that scores the harmfulness of the victim model's responses.
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset.
Learning to Model the World with Language
While current agents can learn to execute simple language instructions, we aim to build agents that leverage diverse language -- language like "this button turns on the TV" or "I put the bowls away" -- that conveys general knowledge, describes the state of the world, provides interactive feedback, and more.
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward.
Aligning Text-to-Image Models using Human Feedback
Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function.
Teachable Reinforcement Learning via Advice Distillation
Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate forms of supervision (like binary preferences) is time-consuming and extracts little information from each human intervention.
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time.
Auto-Tuned Sim-to-Real Transfer
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual properties of the real world.
Hierarchical Text Generation using an Outline
We find that the usage of an outline improves perplexity.
Program Language Translation Using a Grammar-Driven Tree-to-Tree Model
The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid set of structural and grammatical rules.
