Search Results for author:
Found 13 papers, 8 papers with code
Interpreting Grokked Transformers in Complex Modular Arithmetic
Grokking has been actively explored to reveal the mystery of delayed generalization.
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs.
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
We show that while existing prompted LMAs (gpt-3. 5-turbo or gpt-4) achieve 94. 0% average success rate on base tasks, their performance degrades to 24. 9% success rate on compositional tasks.
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation.
Ranked #1 on
on Mind2Web
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data.
Collective Intelligence for 2D Push Manipulations with Mobile Robots
While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems.
A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation
The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control.
Generalized Decision Transformer for Offline Hindsight Information Matching
We present Generalized Decision Transformer (GDT) for solving any HIM problem, and show how different choices for the feature function and the anti-causal aggregator not only recover DT as a special case, but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for matching different statistics of the future.
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.
Distributional Decision Transformer for Hindsight Information Matching
Inspired by distributional and state-marginal matching literatures in RL, we demonstrate that all these approaches are essentially doing hindsight information matching (HIM) -- training policies that can output the rest of trajectory that matches a given future state information statistics.
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
These results show which implementation or code details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh Gaussian policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC.
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works.
