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Z

Zhen Zhang

University of California, Santa Barbara

K

Kaiqiang Song

Zoom Video Communications

X

Xun Wang

Zoom Video Communications

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Yebowen Hu

University of Central Florida

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References (33)

[1]
GTM: Simulating the World of Tools for AI Agents
2025Zhenzhen Ren, Xinpeng Zhang et al.
[2]
Adapting Web Agents with Synthetic Supervision
2025Zhaoyang Wang, Yiming Liang et al.
[3]
Simulating Environments with Reasoning Models for Agent Training
2025Yuetai Li, Huseyin Inan et al.
[4]
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment
2025Tianci Liu, Ran Xu et al.
[5]
MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use
2025Weikang Zhao, Xili Wang et al.
[6]
Checklists Are Better Than Reward Models For Aligning Language Models
2025Vijay Viswanathan, Yanchao Sun et al.
[7]
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
2025Anisha Gunjal, Anthony Wang et al.
[8]
τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment
2025Victor Barres, Honghua Dong et al.
[9]
Interleaved Reasoning for Large Language Models via Reinforcement Learning
2025Roy Xie, David Qiu et al.
[10]
Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models
2025Ilgee Hong, Changlong Yu et al.
[11]
Group-in-Group Policy Optimization for LLM Agent Training
2025Lang Feng, Zhenghai Xue et al.
[12]
BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
2025Jason Wei, Zhiqing Sun et al.
[13]
R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents
2025Naman Jain, Jaskirat Singh et al.
[14]
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
2025Akshara Prabhakar, Zuxin Liu et al.
[15]
Rubric Is All You Need: Improving LLM-Based Code Evaluation With Question-Specific Rubrics
2025Aditya Pathak, Rachit Gandhi et al.
[16]
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
2025Bowen Jin, Hansi Zeng et al.
[17]
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
2025Yuxiang Wei, Olivier Duchenne et al.
[18]
Humanity's Last Exam
2025Long Phan, Alice Gatti et al.
[19]
A Survey on Multi-Turn Interaction Capabilities of Large Language Models
2025Chen Zhang, Xinyi Dai et al.
[20]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey

Showing 20 of 33 references

Founder's Pitch

"CM2 leverages checklist rewards in RL to optimize AI agents for complex multi-step tool interaction tasks."

Reinforcement LearningScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

Sources used for this analysis

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Why It Matters

This research enables the development of AI agents capable of more sophisticated interactions through multi-turn, multi-step reasoning using tools, crucial for domains where explicit rewards are not feasible.

Product Angle

Commercialize as a software package for developing intelligent virtual assistants that perform complex queries over multiple datasets and tools, using checklist-based training to enhance reliability and efficiency.

Disruption

Replaces traditional chatbots with static script paths, offering more dynamic, tool-using interactions without needing exhaustive manual scripting.

Product Opportunity

Target enterprises and platforms that rely on AI-driven customer interaction and require multi-turn, tool-using capabilities. Enterprises pay for increased automation and customer engagement capabilities.

Use Case Idea

Developing virtual assistants in customer service that efficiently manage multi-step tasks using integrated databases and APIs without scripting explicit rewards.

Science

The paper proposes CM2, a reinforcement learning framework that uses checklist rewards instead of traditional verifiable rewards. It decomposes the agent's tasks into fine-grained binary criteria, evaluated in a simulated tool environment to enhance training stability and scalability.

Method & Eval

Tested using an 8k-example RL dataset on various benchmarks improving over a supervised fine-tuned model by 8-12 points, matched and sometimes exceeded open-source baselines.

Caveats

The heavy reliance on LLMs for simulation and evaluation could introduce biases if not managed properly, and the model's efficiency in a real-world setting may vary from simulations.

Author Intelligence

Zhen Zhang

University of California, Santa Barbara

Kaiqiang Song

Zoom Video Communications

Xun Wang

Zoom Video Communications

Yebowen Hu

University of Central Florida

Weixiang Yan

University of California, Santa Barbara

Chenyang Zhao

University of California, Los Angeles

Henry Peng Zou

University of Illinois Chicago

Haoyun Deng

Zoom Video Communications

Sathish Reddy Indurthi

Zoom Video Communications

Shujian Liu

Zoom Video Communications

Simin Ma

Zoom Video Communications

Xiaoyang Wang

Zoom Video Communications

Xin Eric Wang

University of California, Santa Barbara

Song Wang

Zoom Video Communications