ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

arXiv:2609.19134 · cs.CL, cs.CY · Submitted 2026-09-16 · Read on arXiv

cs.CL, cs.CY

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Code: https://github.com/aitofound/ScienceIDE

Code: https://github.com/aitofound/ScienceIDE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 37× in this paper · explained in 2 episodes
• Verifier — 31× in this paper · explained in 2 episodes
• Reinforcement Learning — 27× in this paper · explained in 4 episodes
• Core — 12× in this paper · explained in 2 episodes
• Verification — 12× in this paper · explained in 2 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Adam — 8× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Benchmarking — 4× in this paper · explained in 4 episodes
• Trace — 4× in this paper · explained in 2 episodes
• GRPO — 3× in this paper · explained in 6 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Agent Interactions — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Verifiable Rewards — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Symbolic Regression — 1× in this paper · explained in 5 episodes

Sources

• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
• The Arcade Learning Environment: An Evaluation Platform for General Agents
• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
• Emergent autonomous scientific research capabilities of large language models
• TorchRL: A data-driven decision-making library for PyTorch
• ChemCrow: Augmenting large-language models with chemistry tools
• OpenAI Gym
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• SWE-Universe: Scale Real-World Verifiable Environments to Millions
• Deep reinforcement learning from human preferences
• Leveraging Procedural Generation to Benchmark Reinforcement Learning
• TextWorld: A Learning Environment for Text-based Games
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab
• AInsteinBench: Benchmarking Coding Agents on Scientific Repositories
• MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
• SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
• Robin: A multi-agent system for automating scientific discovery
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

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