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v0.3.0

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

Release Assets

GitHub Release v0.3.0

Fixed PyPI package distribution that was broken in v0.2.0. No functional changes — all features and fixes from v0.2.0 apply.

NeMo Gym v0.2.0 ships alongside the NVIDIA Nemotron 3 Super model release, open sourcing the RL environments and corresponding datasets used during training. This release adds 17 new training environments across coding, math, science, reasoning, agentic tasks, and safety, plus integrations with Aviary, Reasoning Gym, and Verifiers to combine additional environments. You can now run end-to-end rollout collection locally with vLLM and install directly from PyPI.

New Environments

Added 17 new resources servers spanning:

  • Coding: Text to SQL, SWE RL Gen, SWE RL LLM Judge
  • Math: Lean4 Mathematical Proofs
  • Science: Aviary, NewtonBench
  • Reasoning: MultiChallenge, ARC-AGI
  • Agent tasks: xLAM Function Calling, Tavily Search, Single Step Tool Use, Terminus Judge, NeMo Skills Tools
  • Safety: Jailbreak Detection, Over Refusal Detection
  • RLHF: Generative Reward Model Compare

Added 5 new agent servers: Aviary agent, proof refinement agent, SWE agents, tool simulation agent, and verifiers agent.

Environment library integrations: Future House Aviary, Open-Thought Reasoning Gym, Prime Intellect Verifiers.

Model Serving

  • Local vLLM model server with end-to-end rollout collection without an external API
  • vLLM 0.16+ support for the reasoning field in responses
  • Per-task chat templates and extra body args to support different model configurations across environments in multi-environment training

Rollout Collection & Profiling

  • New ng_reward_profile command to compute per-task pass rates and aggregate metrics
  • CPU profiling for rollout performance analysis
  • Seeding on num_repeats for reproducible rollouts

Infrastructure & Developer Experience

  • PyPI compatibility: install via pip install nemo-gym
  • Dry run mode: ng_run +dry_run=true to validate configs and install environments without starting servers
  • ng_status command to list running servers and their health
  • FastAPI worker support for higher throughput across multiple workers
  • Server stdout/stderr redirection with server name prefixes

Model Recipes

  • Nemotron 3 Nano 30B end-to-end training recipe with single-GPU and multi-node tutorials

Documentation

  • Added training tutorials for Unsloth, TRL, and Nemotron 3 Nano (single-GPU and multi-node)
  • Added environment tutorials for creating environments, custom data preparation, and integrating external libraries
  • Rewrote concepts documentation with new training approaches page, architecture diagrams, and expanded agent/resources server docs
  • Revamped ecosystem page with training framework and environment library integrations
  • Added deployment topology and SWE RL infrastructure case study
  • Site-wide quality sweep: consistent naming, style guide, redirects, and FAQ additions

Bug Fixes

  • Fixed 0.1.1 environments to work correctly with RL training pipelines
  • Fixed crash when server receives malformed JSON during rollout collection
  • Fixed dry run mode failing after initial implementation
  • Fixed nested responses_create_params overrides not merging correctly from CLI
  • Fixed ng_prepare_data failing when multiple environments define overlapping metrics
  • Fixed reward profiling failing when model response doesn’t include usage stats
  • Fixed NeMo-Skills python tool to use HTTP calls instead of subprocess execution
  • Bumped Pillow and other packages to address security vulnerabilities
  • ng_dump_config now redacts API key values from output

First-Time Contributors

We’d like to highlight the following first-time contributors:

  • @sidnarayanan added the Aviary integration to enable training on any Aviary environment, a library of interactive RL environments spanning math, science, biology, and more
  • @3mei added the text-to-SQL environment to generate SQL queries from natural language across multiple SQL dialects
  • @Kelvin0110 added the NewtonBench environment to discover scientific laws through interactive experimentation

Initial public release of NeMo Gym.