DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback (2410.06215v3)

Abstract: The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model. Approaches using LLMs as annotators reduce human effort, but still require humans to interpret feedback from evaluations and control the LLM to produce data the student needs. Automating this labor-intensive process by creating autonomous data generation agents - or teachers - is desirable, but requires environments that can simulate the feedback-driven, iterative, closed loop of data creation. To enable rapid, scalable testing for such agents and their modules, we introduce DataEnvGym, a testbed of teacher environments for data generation agents. DataEnvGym frames data generation as a sequential decision-making task, involving an agent consisting of a data generation policy (which generates a plan for creating training data) and a data generation engine (which transforms the plan into data), inside an environment that provides student feedback. The agent's goal is to improve student performance. Students are iteratively trained and evaluated on generated data, and their feedback (in the form of errors or weak skills) is reported to the agent after each iteration. DataEnvGym includes multiple teacher environment instantiations across 3 levels of structure in the state representation and action space. More structured environments are based on inferred skills and offer more interpretability and curriculum control. We support 4 domains (math, code, VQA, and tool-use) and test multiple students and teachers. Example agents in our teaching environments can iteratively improve students across tasks and settings. Moreover, we show that environments teach different skill levels and test variants of key modules, pointing to future work in improving data generation agents, engines, and feedback mechanisms.

• The paper introduces DataEnvGym, a framework that automates training data creation by leveraging iterative teacher environments and student feedback.
• It details structured environments, including open-ended, skill-list, and skill-tree approaches, to precisely generate data tailored to model needs.
• Experimental results across mathematics, visual question answering, and programming show improved model performance, underscoring the role of state-dependent decisions.

Insights into "DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback"

The paper "DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback" introduces a structured framework for advancing the automation of training data creation for machine learning models. The authors propose DataEnvGym, a testbed designed to facilitate the development and evaluation of autonomous data generation agents, referred to as teachers, through integration into modular environments.

Summary of DataEnvGym

The goal of DataEnvGym is to automate the labor-intensive process of generating training data typically driven by human intervention. This framework situates data generation within a feedback-driven, iterative environment where a teacher agent is responsible for enhancing the performance of a student model. It frames data generation as a sequential decision-making task, involving a data generation policy and engine, within environments that offer feedback based on the student model’s performance.

Key Features of the Framework

1. Teacher Environments: DataEnvGym provides environments that simulate feedback-driven processes to optimize data generation. The environment evaluates the performance of student models, facilitating learning through iterative cycles.
2. Structured and Flexible Environments: The paper discusses three instantiations of teacher environments:
   • Open-Ended Environment: Offers a straightforward approach without explicit skill structures, allowing unrestricted data generation.
   • Skill-List Environment: Enforces a structured skill-based approach where student performance is evaluated on predefined skills.
   • Skill-Tree Environment: Uses hierarchical skill representations to guide and control the data generation process more precisely.
3. Agents and Modules: Each environment uses agents composed of data generation policies and engines, integrated with modules for training, student evaluation, skill discovery, and skill organization.

Experimental Results

The framework was tested across three domains: mathematics, visual question answering (VQA), and programming. Results indicated that DataEnvGym’s structured environments are effective at improving student model performance. Notably, the performance improvement is lessened when the teacher model lacks state information, highlighting the relevance of state-dependent decision-making in generating effective training data.

Implications and Potential Developments

DataEnvGym’s modular nature makes it a versatile tool for testing different data generation strategies and modules. Its ability to integrate with various tasks and settings marks an advancement in developing autonomous systems capable of iterative self-improvement. The framework encourages future exploration into more complex skill discovery mechanisms and further refinement of data generation policies and engines. It also paves the way for enhancing model robustness by efficiently addressing model weaknesses.

Conclusion

By emphasizing structured skill representation and environment-specific agent actions, DataEnvGym addresses a critical gap in automating the evaluation-driven data creation process for machine learning models. The work stands as a significant foundation for future research focused on reducing manual oversight in training data generation and enriches the toolkit available for AI model improvement through data-centric approaches.