2025
ICML 2025 · TTODLer-FM Workshop · Oral
Too Big to Think: Capacity, Memorization, and
Generalization in Pre-Trained Transformers
Joshua Barron, Devin White
Controlled character-level pretraining experiments
examine how model capacity changes the balance between
arithmetic extrapolation and factual memorization. The
results identify a narrow generalization window that
collapses when factual knowledge is introduced.
My contribution:
Advised on research framing, interpretation of
experimental results, and communication of the
capacity–generalization findings.
LLMs
Small models
Generalization
2025
ICML 2025 · Models of Human Feedback Workshop
Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern,
Nicholas Waytowich, Yongcan Cao
Combines rating classification and reward regression
with learnable uncertainty weights, providing a more
label-efficient approach to reward modeling from human
ratings.
My contribution:
Contributed to problem formulation, investigation and
interpretation of results, research direction, and
manuscript development.
RLHF
Ratings
Reward modeling
2025
NeurIPS 2025 · ARLET Workshop
Human-Inspired Multi-Level Reinforcement Learning
Mingkang Wu, Devin White, Vernon Lawhern,
Nicholas Waytowich, Yongcan Cao
Uses rated performance levels to guide both reward
learning and policy improvement, including information
from failed and lower-performing experiences rather than
learning only from successful behavior.
My contribution:
Contributed to problem definition, investigation of
results, research direction, next-step decisions, and
manuscript development for the expanded multi-level
framework.
RLHF
Ratings
Policy learning
2025
AAAI 2025 · CAIHU Bridge
Performance Optimization of Ratings-Based
Reinforcement Learning
Evelyn Rose, Devin White, Mingkang Wu, Vernon Lawhern,
Nicholas Waytowich, Yongcan Cao
Systematically investigates design choices that affect
the consistency and performance of Rating-Based
Reinforcement Learning, including rating boundaries,
smoothing, and the number of rating classes.
My contribution:
Helped define the research questions, investigate
consistency and performance tradeoffs, determine next
experiments, and develop the manuscript.
RLHF
Ratings
Evaluation
2025
AAAI 2025 · KnowFM Workshop
Atari-GPT: Benchmarking Multimodal Large Language
Models as Low-Level Policies in Atari Games
Nicholas R. Waytowich, Devin White, MD Sunbeam,
Vinicius G. Goecks
Introduces a benchmark for evaluating multimodal
language models as low-level controllers in Atari-style
environments, exposing limitations in visual grounding,
action consistency, and real-time interaction.
My contribution:
Helped define the research direction; developed code and
environments; ran experiments; and contributed to
analysis and writing.
LLMs
Agents
Benchmarking
2024
AAAI 2024
Rating-Based Reinforcement Learning
Devin White, Mingkang Wu, Ellen Novoseller,
Vernon J. Lawhern, Nicholas Waytowich, Yongcan Cao
Proposes learning reward functions from direct ratings
of individual trajectories rather than pairwise
preferences, supported by synthetic-feedback experiments
and an IRB-approved human-subject study.
My contribution:
Helped originate the research direction and led algorithm
design, implementation, experimental planning, analysis,
and publication.
RLHF
Ratings
Human study
2024
AIAA SciTech 2024 · GNC
Deep Reinforcement Learning-based Optimal
Time-constrained Intercept Guidance
Abhinav Sinha, Devin White, Yongcan Cao
Uses proximal policy optimization to select
guidance-law design parameters for energy-efficient
rendezvous under a specified arrival-time constraint.
My contribution:
Contributed to reinforcement-learning system development
and experimental analysis for the autonomous guidance
problem.
Reinforcement learning
Control
Autonomy
2023
ICML 2023 · Workshop
Rating-based Reinforcement Learning
Devin White, Mingkang Wu, Ellen Novoseller,
Vernon Lawhern, Nicholas Waytowich, Yongcan Cao
Early workshop presentation of the direct-rating
framework that later developed into the full AAAI 2024
Rating-Based Reinforcement Learning paper.
My contribution:
Led early method development and evaluation that
established the foundation for the later full-conference
work.
RLHF
Ratings
Reinforcement learning