Can we automate the process of generating data to improve a model on diverse, open-ended tasks, based on automatically-discovered model weaknesses? Introducing DataEnvGym - a testbed for data-generation agents + teaching environments. Environment trains/evaluates student model ➡️ Environment discovers skills/errors and gives feedback to agent ➡️ Agent generates updated training data to address weaknesses ➡️ Iterate Key Idea -- Frame data generation + model improvement as an RL-style sequential decision-making task: states encode student errors, policy decides actions encoding which data to generate, and reward is the performance of the student model. We provide several modular environments + teaching agents that can improve models on VQA/math/programming, and provide a leaderboard benchmarking these agents. We welcome more entries to our leaderboard! Thread 🧵👇 (1/9)

🚨 Excited to share: "Learning to Generate Unit Tests for Automated Debugging" 🚨 which introduces ✨UTGen and UTDebug✨ for teaching LLMs to generate unit tests (UTs) and debugging code from generated tests. UTGen+UTDebug improve LLM-based code debugging by addressing 3 key questions: 1⃣ What are desirable properties of unit test generators? (A: high output acc and rate of uncovering errors) 2⃣ How good are models at 0-shot unit test generation (A: they are not great) ... so how do we improve LLMs' UT generation abilities? (A: bootstrapping from code-generation data via UTGen) 3⃣ How can we use potentially noisy feedback from generated tests for debugging? (A: via test-time scaling and validation + backtracking in UTDebug) 🧵👇