Agentick: A Unified Benchmark for General Sequential Decision-Making Agents

2026-05-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Agentick is a new unified benchmark for evaluating sequential decision-making AI agents, including Reinforcement Learning (RL), Large Language Models (LLMs), Vision-Language Models (VLMs), hybrid, and human agents. It features 37 procedurally generated tasks across six capability categories (navigation, planning, reasoning, memory, generalization, multi-agent), four difficulty levels, and five observation modalities, all accessible via a Gymnasium-compatible interface. The benchmark includes a Coding API, oracle reference policies, pre-built Supervised Fine-Tuning (SFT) datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes revealed that no single approach dominates, with GPT-5 mini achieving an overall 0.309 oracle-normalized score, while PPO excels in planning and multi-agent tasks. The study also found that chain-of-thought reasoning harnesses boost LLM performance by 3–10x, and ASCII observations consistently outperform natural language for LLM agents.

Key takeaway

For AI researchers and engineers developing autonomous agents, Agentick provides a critical tool for fair, multi-paradigm evaluation. Your teams should leverage its capability-decomposed scoring and multi-modal observations to diagnose agent strengths and weaknesses, especially when exploring hybrid architectures or optimizing prompting strategies for LLMs. The benchmark's training infrastructure and oracle datasets also offer a robust foundation for RL post-training of foundation models in complex, interactive environments, pushing beyond current single-turn RLVR limitations.

Key insights

Agentick unifies evaluation for diverse AI agents in sequential decision-making across varied tasks and modalities.

Principles

• Paradigm universality ensures fair comparison.
• Capability decomposition provides diagnostic insights.
• Training-first design supports research iteration.

Method

Agentick evaluates agents using a unified Gymnasium-compatible interface across 37 procedurally generated tasks, five observation modalities, and four difficulty levels, normalizing scores against oracle performance.

In practice

• Use ASCII observations for LLM spatial reasoning.
• Implement chain-of-thought prompting for LLM agents.
• Consider hybrid architectures for broad capability.

Topics

• Agentick Benchmark
• Sequential Decision-Making
• Foundation Models
• Reinforcement Learning
• Multi-modal Observations

Code references

• open-thought/reasoning-gym
• samvelyan/minihack

Best for: NLP Engineer, Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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