Adapting Procedural RLVR Environments to Knowledge-Intensive STEM Domains

Investigate how to adapt procedural generation methods for synthetic reinforcement learning environments—currently effective for algorithmic tasks such as mathematics and code—to cover knowledge-intensive STEM domains including medicine, economics, and cybersecurity.

Background

The paper evaluates RLVE and other procedural generation approaches that create synthetic RLVR environments, noting strong performance in algorithmic domains such as math and code but limited gains in STEM. The authors highlight that extending these approaches beyond formal, algorithmic tasks into knowledge-rich areas remains challenging.

This unresolved question is central to scaling RLVR beyond traditional domains, particularly where verification is difficult and domain knowledge is extensive (e.g., medicine, economics, cybersecurity). The proposed GooseReason pipeline addresses data scarcity via MCQ fill-in-the-middle tasks from unverifiable text, but the broader problem of adapting procedural generation itself to these domains remains open.