Integrate with Prime Intellect verifiers

[cwing-nvidia]

Use cases, pain points, and background While they differ in their architectural approach, both frameworks target the same goal: enabling Reinforcement Learning from Verifiable Reward (RLVR) at scale.

Description: Goal: Bidirectional interoperability such that environments authored in either framework can be used by the other.

• Expanded Environment Library
  • Users of either framework gain access to environments from both: NeMo Gym and Prime Intellect Environments Hub.
• Shared Ecosystem Growth
  • A unified foundation means innovation from either community benefits both: more environments and RLVR datasets.
• Framework Flexibility
  • Researchers can author in whichever framework fits their workflow, and their environments will work across both ecosystems.

Design: What files should be touched? What logic should be written?

Out of scope: What are some items that this issue could be mistaken to cover that this issue should explicitly NOT cover?

Acceptance Criteria:

• [ ] Individual items that need to be finished in order for this issue to be considered completed