Skip to content

v0.3.0

Latest
Latest

Choose a tag to compare

View all tags
@nemo-automation-bot nemo-automation-bot released this 04 Jun 15:53
v0.3.0
4c44cf9
This commit was signed with the committer’s verified signature.
kajalj22
GPG key ID: 3CD5ACB99A778B34
Verified
Learn about vigilant mode.

Release Summary

NeMo Gym v0.3.0 ships alongside the NVIDIA Nemotron 3 Ultra model release, open sourcing the environments and corresponding datasets used during training.

Highlights:

  • 70+ new environments, including benchmarks such as Tau2 and Nemotron RL training environments
  • Popular harness available out-of-the-box such as Claude Code and Hermes
  • Integrations with OpenEnv and Harbor - use environments from these libraries directly with NeMo Gym
  • Integration with VeRL - train with VeRL and scale rollout collection with NeMo Gym

First-Time Contributors

We welcomed 30+ new contributors to this release! Here are a few highlights:

  • @grace-lam added the integration to run Harbor environments with NeMo Gym
  • @aleksficek — added Competitive Coding Challenges environment
  • @jthomson04 improved rollout resilience when models emit malformed tool-call arguments or missing message content

Thank you to all the new contributors for helping make NeMo Gym better!

New Environments & Benchmarks

Added 70+ new environments including novel datasets and integrations of popular benchmarks. New coverage spans:

  • Coding — competitive programming, code infilling, SQL generation, and software-engineering benchmarks with execution-based verification
  • Math & proofs — olympiad-style problems, proof grading and validation, and formal verification (including Lean)
  • Knowledge & science — graduate-level QA, chemistry and physics tasks, and lab-style reasoning (including multimodal figure, table, and protocol tasks)
  • Agentic — multi-turn tool use, search, sandboxed execution, finance workflows, and tau-bench-style conversational agents
  • Instruction following — format constraints, citation compliance, and IFBench-style rule verification
  • Safety & RLHF — jailbreak detection, abstention calibration, prompt-injection resistance, and generative reward modeling
  • Multimodal, speech & translation — VLM benchmarks, visual grounding, ASR evaluation, and machine-translation quality metrics
  • Chat & broad knowledge — arena-style preference evaluation and MMLU-family benchmarks
  • Interactive RL — Gymnasium-style multi-step environments for spatial and game-based training

See the Available Environments table for the full list.

Configure Agent Harnesses

  • Claude Code — available out of the box in NeMo Gym
  • Hermes — available out of the box in NeMo Gym
  • LangGraph agent — an adapter that lets you build custom agents using LangGraph patterns (reflection, subagent orchestration, parallel thinking, rewoo)
  • Gymnasium agent — generic multi-turn harness for use with OpenAI Gym-style environments

Configure Models

  • Optional max_concurrent_requests on the OpenAI model server to cap in-flight API calls — useful for rate-limited external endpoints when rollout concurrency is high

Rollout Collection & Profiling

  • New ng_aggregate_rollouts command to merge rollout shards collected independently across multiple nodes, enabling distributed eval without requiring a single coordinated collection job

Environment Library Integrations

  • OpenEnv — combine OpenEnv environments with NeMo Gym environments
  • Harbor — combine Harbor environments with NeMo Gym environments

Deprecation Notices

  • Documentation has moved from Sphinx to Fern. Old Sphinx URLs redirect to the new site at docs.nvidia.com/nemo/gym. The docs/ directory is no longer used for publishing.

Bug Fixes

  • Fixed aiohttp connection limit exhaustion under FastAPI/Uvicorn with multiple workers
  • Fixed session cookie propagation for Starlette >= 1.0.0
  • Fixed duplicated usage counting and errors on empty usage in subsequent model calls
  • Improved rollout resilience when models emit malformed tool-call arguments or missing message content
  • Fixed prompt-key hashing when inputs contain Pydantic BaseModel objects

Documentation

  • New concepts pages for environments, evaluation, and training
  • Improved Architecture page to clarify how environments map to NeMo Gym components
  • Consolidated detailed setup and quickstart into a single improved quickstart with clearer descriptions
  • Expanded Ecosystem page with environment library, training framework, and agent harness integrations
Changelog Details

Contributors

odelalleau, marta-sd, and 51 other contributors
Assets 2
Loading