STACX: A Modular Infrastructure for End-to-End Agentic RL
daibond_alpha · x · 2026-08-12
STACX is an open-source modular framework designed for agent training, evaluation, and reinforcement learning. It establishes a complete closed-loop pipeline: benchmark tasks are assigned to agents operating within real sandbox containers, scored by verifiers, and the resulting traces are fed back to the trainer.
Key Features:
- Training & Evaluation: Supports various training recipes including GRPO, SFT, and DAgger. Compatible with existing scaffolds like OpenHands and native agents.
- Multi-Benchmark: Capable of running across diverse environments such as SWE-bench, Terminal-Bench, and KernelBench.
- Architecture: Integrates sandbox, environment, and RL engines to address heterogeneity across agents, environments, and algorithms, significantly improving the efficiency of LLM pretraining and agent research.
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