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Zhening Li

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2 papers

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NeurIPS Conference 2025 Conference Paper

EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths

Zhening Li
Armando Solar-Lezama
Yisong Yue
Stephan Zheng

We introduce a new approach to agent programming, the development of LLM-based agents. Current approaches to agent programming often entangle two aspects of agent design: the core workflow logic and the inference-time strategy (e. g. , tree search). We introduce probabilistic angelic nondeterminism (PAN), a programming model that disentangles these two concerns, allowing the programmer to describe the agent workflow and independently experiment with different inference-time strategies by simply changing a few inputs. We provide an implementation of PAN in Python as the EnCompass framework, which uses a Python decorator to compile agent workflow programs into a search space. We present three case studies that demonstrate how the framework lets the programmer quickly improve the reliability of an agent and easily switch between different inference-time strategies, all with little additional coding.

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ICML Conference 2024 Conference Paper

When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions

Zhening Li
Gabriel Poesia
Armando Solar-Lezama

Skills are temporal abstractions that are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an environment that make skills useful, a precise characterization has been absent. We provide the first such characterization, focusing on the utility of deterministic skills in deterministic sparse-reward environments with finite action spaces. We show theoretically and empirically that RL performance gain from skills is worse in environments where solutions to states are less compressible. Additional theoretical results suggest that skills benefit exploration more than they benefit learning from existing experience, and that using unexpressive skills such as macroactions may worsen RL performance. We hope our findings can guide research on automatic skill discovery and help RL practitioners better decide when and how to use skills.

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