2 citations · 4 across the 11 of their papers we have counts for
12 papers
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Bo Liu, Simon Yu, Yiding Jiang +15
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, stat…
Maximum Likelihood Reinforcement Learning
Fahim Tajwar, Guanning Zeng, Yueer Zhou +7
Reinforcement learning (RL) is the method of choice for training models in setups where the objective function can only be evaluated by sampling from the model. Our key observation…
From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
Marc Finzi, Shikai Qiu, Yiding Jiang +3
Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to exist…
MA-SLAM: Active SLAM in Large-Scale Unknown Environment using Map Aware Deep Reinforcement Learning
Yizhen Yin, Yuhua Qi, Dapeng Feng +4
Active Simultaneous Localization and Mapping (Active SLAM) involves the strategic planning and precise control of a robotic system's movement in order to construct a highly accurat…
Learning Parameterized Skills from Demonstrations
Vedant Gupta, Haotian Fu, Calvin Luo +2
We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy…
Looking beyond the next token
Abitha Thankaraj, Yiding Jiang, J. Zico Kolter +1
The structure of causal language model training assumes that each token can be accurately predicted from the previous context. This contrasts with humans' natural writing and reaso…
Safety Pretraining: Toward the Next Generation of Safe AI
Pratyush Maini, Sachin Goyal, Dylan Sam +7
As large language models (LLMs) are increasingly deployed in high-stakes settings, the risk of generating harmful or toxic content remains a central challenge. Post-hoc alignment m…
Training a Generally Curious Agent
Fahim Tajwar, Yiding Jiang, Abitha Thankaraj +4
Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic inf…
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale
Andrew Jesson, Yiding Jiang
We demonstrate that recent advances in reinforcement learning (RL) combined with simple architectural changes significantly improves generalization on the ProcGen benchmark. These…
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
Yiding Jiang, Allan Zhou, Zhili Feng +2
The composition of pretraining data is a key determinant of foundation models' performance, but there is no standard guideline for allocating a limited computational budget across…
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Yutong He, Alexander Robey, Naoki Murata +7
Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development o…
Understanding prompt engineering may not require rethinking generalization
Victor Akinwande, Yiding Jiang, Dylan Sam +1
Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performan…