EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation

2025-05-01 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

EnvSimBench is a new benchmark designed to evaluate and improve the Environment Simulation Ability (EnvSim Ability) of Large Language Models (LLMs) for training AI agents. It addresses issues like hallucination, logical inconsistencies, and silent state drift in LLM-simulated environments. The benchmark comprises 400 samples across 167 diverse tool-interactive environments, with verifiable labels and difficulty stratification along three axes: action outcome, state-change complexity, and argument cardinality. Evaluations of seven frontier LLMs reveal a "state-change cliff," where models achieve near-perfect accuracy on state-invariant tasks but fail catastrophically when multiple states require simultaneous updates. To mitigate this, a constraint-driven simulation pipeline was developed, which significantly reduces hallucination, boosts environment synthesis yield by 6.8%, and cuts costs by over 90%. The code and data for EnvSimBench are publicly available.

Key takeaway

Research Scientists developing LLM-based agent training environments should prioritize evaluating simulation fidelity using metrics like Config Match (CM) rather than just Feedback Match (FM). Be aware of the "state-change cliff" where LLMs fail on tasks requiring three or more simultaneous state updates. Consider adopting a constraint-driven MDP formulation and fine-tuning smaller, specialized models, as this approach has been shown to surpass frontier LLMs in CM and reduce costs by over 90%.

Key insights

LLMs struggle with accurate environment simulation, especially with complex state changes, necessitating specialized benchmarks and constraint-driven methods.

Principles

• EnvSim Ability is distinct from general reasoning.
• Explicit state and logic prevent simulation failures.
• Balanced data composition improves generalization.

Method

EnvSimBench reframes environment simulation as a fully observable Markov Decision Process (MDP) task, providing explicit before-state, action, and implementation logic to the LLM for single-turn state prediction.

In practice

• Use Config Match (CM) over Feedback Match (FM) for fidelity.
• Implement constraint-driven prompts for LLM simulators.
• Fine-tune small models with balanced data for cost-efficiency.

Topics

• EnvSimBench
• LLM Environment Simulation
• Environment Simulation Ability
• State-Change Cliff
• Constraint-Driven Simulation

Code references

• cookieApril/EnvSimBench

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.