arXiv subjects

Richard Yang

Publications and source records attributed to Richard Yang.

5 recordsLinked to original sources

arXiv2026

From LLM-Generated Specifications to Learned Quadruped Locomotion

Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. Shaped rewards from formal specifications in languages like Signal Temporal Logic (STL) can make rewards more interpretable, but writing STL specifications itself still requires domain expertise. We study whether large language models (LLMs) can fill this gap by generating Parametric Signal Temporal Logic (PSTL) specifications that are subsequently used for policy learning. Given a natural language locomotion objective and a constrained specification grammar, GPT-5.5 and Qwen 3.6 independently propose STL templates for command tracking, safety, and gait structure. We instantiate the parameters of the generated PSTL templates using expert trajectories and retain only specifications that are consistent with demonstrated expert behavior. The resulting specifications are then transformed into smooth, finite-history reward functions and used to train a quadruped locomotion policy with Proximal Policy Optimization (PPO) in MuJoCo XLA (MJX). We evaluate both \emph{gait-aware} and \emph{gait-agnostic} settings. The former specifies walking-trot, trot, and bound regimes, while the latter allows contact patterns to emerge from the task objective. We compare against hand-engineered rewards, Text2Reward-style LLM-generated reward code, and an expert-switching oracle. Gait-aware Qwen 3.6 specifications achieved 100\% survival and command success across all tested speeds (0.3--2.1 m/s) and matched the target gait at high speeds, whereas Text2Reward achieved 0\% for both metrics at $\geq 1.9$ m/s. Videos: https://stl-locomotion.github.io/

cs.RO↗

arXiv2025

PokerBench: Training Large Language Models to become Professional Poker Players

We introduce PokerBench - a benchmark for evaluating the poker-playing abilities of large language models (LLMs). As LLMs excel in traditional NLP tasks, their application to complex, strategic games like poker poses a new challenge. Poker, an incomplete information game, demands a multitude of skills such as mathematics, reasoning, planning, strategy, and a deep understanding of game theory and human psychology. This makes Poker the ideal next frontier for large language models. PokerBench consists of a comprehensive compilation of 11,000 most important scenarios, split between pre-flop and post-flop play, developed in collaboration with trained poker players. We evaluate prominent models including GPT-4, ChatGPT 3.5, and various Llama and Gemma series models, finding that all state-of-the-art LLMs underperform in playing optimal poker. However, after fine-tuning, these models show marked improvements. We validate PokerBench by having models with different scores compete with each other, demonstrating that higher scores on PokerBench lead to higher win rates in actual poker games. Through gameplay between our fine-tuned model and GPT-4, we also identify limitations of simple supervised fine-tuning for learning optimal playing strategy, suggesting the need for more advanced methodologies for effectively training language models to excel in games. PokerBench thus presents a unique benchmark for a quick and reliable evaluation of the poker-playing ability of LLMs as well as a comprehensive benchmark to study the progress of LLMs in complex game-playing scenarios.

cs.CL↗

arXiv2024

GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset

Analyzing individual human trajectory data helps our understanding of human mobility and finds many commercial and academic applications. There are two main approaches to accessing trajectory data for research: one involves using real-world datasets like GeoLife, while the other employs simulations to synthesize data. Real-world data provides insights from real human activities, but such data is generally sparse due to voluntary participation. Conversely, simulated data can be more comprehensive but may capture unrealistic human behavior. In this Data and Resource paper, we combine the benefit of both by leveraging the statistical features of real-world data and the comprehensiveness of simulated data. Specifically, we extract features from the real-world GeoLife dataset such as the average number of individual daily trips, average radius of gyration, and maximum and minimum trip distances. We calibrate the Pattern of Life Simulation, a realistic simulation of human mobility, to reproduce these features. Therefore, we use a genetic algorithm to calibrate the parameters of the simulation to mimic the GeoLife features. For this calibration, we simulated numerous random simulation settings, measured the similarity of generated trajectories to GeoLife, and iteratively (over many generations) combined parameter settings of trajectory datasets most similar to GeoLife. Using the calibrated simulation, we simulate large trajectory datasets that we call GeoLife+, where + denotes the Kleene Plus, indicating unlimited replication with at least one occurrence. We provide simulated GeoLife+ data with 182, 1k, and 5k over 5 years, 10k, and 50k over a year and 100k users over 6 months of simulation lifetime.

cs.DB↗

arXiv2024

Towards Deep Application-Network Integration: Architectures, Progress and Opportunities

With the rise of a new generation of applications (e.g., virtual and augmented reality, artificial intelligence, etc) demanding stringent performance requirements, the need for networking solutions and architectures that can enable a higher Quality of Experience (QoE) is becoming increasingly important. While jointly optimizing application and network may increase the applications' QoE and simultaneously improve the utilization of network resources, such a paradigm has had limited success in real production networks. However, with the combination of revolutionary trends in (1) compute processing demands, (2) networking capabilities, and (3) sustainable business models, it is high time the community explores the full potential of deeper integration between application and network. In this paper, recent trends observed over the past few years are systematically reviewed. These include the paradigm shift in modern communication services towards computing-driven applications, such as on-site AI training, advances in programmable network technologies like Software Defined Networking (SDN), and new business models incentivizing collaboration and cooperation between parties. Following this, successful scenarios that benefit from various forms of deeper network-application integration are reported, highlighting their considerable potential. A unified framework is then introduced, providing an overview of possible architecture paradigms for network-application integration and bringing awareness to existing abstractions, mechanisms, tools, and their potential combinations. The paper concludes with a discussion of several remaining challenges in building practical network-application integrated systems.

cs.NI↗

arXiv2020

Carbide: Highly Reliable Networks Through Real-Time Multiple Control Plane Composition

Achieving highly reliable networks is essential for network operators to ensure proper packet delivery in the event of software errors or hardware failures. Networks must ensure reachability and routing correctness, such as subnet isolation and waypoint traversal. Existing work in network verification relies on centralized computation at the cost of fault tolerance, while other approaches either build an over-engineered, complex control plane, or compose multiple control planes without providing any guarantee on correctness. This paper presents Carbide, a novel system to achieve high reliability in networks through distributed verification and multiple control plane composition. The core of Carbide is a simple, generic, efficient distributed verification framework that transforms a generic network verification problem to a reachability verification problem on a directed acyclic graph (DAG), and solves the latter via an efficient distributed verification protocol (DV-protocol). Equipped with verification results, Carbide allows the systematic composition of multiple control planes and realization of operator-specified consistency. Carbide is fully implemented. Extensive experiments show that (1) Carbide reduces downtime by 43% over the most reliable individual underlying control plane, while enforcing correctness requirements on all traffic; and (2) by systematically decomposing computation to devices and pruning unnecessary messaging between devices during verification, Carbide scales to a production data center network.

cs.NI↗