Search Results for author:
Found 11 papers, 9 papers with code
Autonomous Evaluation and Refinement of Digital Agents
We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control.
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
In this paper, we develop a framework for building multi-turn RL algorithms for fine-tuning LLMs, that preserves the flexibility of existing single-turn RL methods for LLMs (e. g., proximal policy optimization), while accommodating multiple turns, long horizons, and delayed rewards effectively.
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
In this work, we propose a new hybrid RL algorithm that combines an on-policy actor-critic method with offline data.
Test-Time Distribution Normalization for Contrastively Learned Vision-language Models
Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking the dot product between image and text representations.
BT^2: Backward-compatible Training with Basis Transformation
In this way, the new representation can be directly compared with the old representation, in principle avoiding the need for any backfilling.
$BT^2$: Backward-compatible Training with Basis Transformation
In this way, the new representation can be directly compared with the old representation, in principle avoiding the need for any backfilling.
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction.
TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment
The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner.
GAPX: Generalized Autoregressive Paraphrase-Identification X
Paraphrase Identification is a fundamental task in Natural Language Processing.
Improve Discourse Dependency Parsing with Contextualized Representations
Recent works show that discourse analysis benefits from modeling intra- and inter-sentential levels separately, where proper representations for text units of different granularities are desired to capture both the meaning of text units and their relations to the context.
Data Driven Modeling of Turbocharger Turbine using Koopman Operator
The objective of this paper is to develop a model to predict the transient and steady-state behavior of the turbine using the Koopman operator which can be helpful for control design and analysis.
