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NeMo Gym (docs, GitHub) is a library for building reinforcement learning environments for large language models. We offer helpers that import NeMo Gym rollout exports into Docent.

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Use this when

Use NeMo Gym ingestion when your source data is either:
  • one parsed rollout dictionary already loaded in Python
  • a JSONL export where each line is one NeMo Gym rollout object

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Main helpers

  • convert_nemogym_rollout_to_agent_run(rollout) converts one parsed rollout object into one AgentRun.
  • convert_nemogym_jsonl_file_to_agent_runs(file_path) reads a JSONL file and converts each line into one AgentRun.
from docent.sdk.integrations import (
    convert_nemogym_jsonl_file_to_agent_runs,
    convert_nemogym_rollout_to_agent_run,
)

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Example

To convert a JSONL export: 
from docent.sdk.integrations import convert_nemogym_jsonl_file_to_agent_runs

agent_runs = convert_nemogym_jsonl_file_to_agent_runs("rollouts.jsonl")
To convert one rollout dictionary already loaded in memory: 
from docent.sdk.integrations import convert_nemogym_rollout_to_agent_run

agent_run = convert_nemogym_rollout_to_agent_run(rollout)
After conversion, upload normally: 
from docent import Docent
from docent.sdk.integrations import convert_nemogym_jsonl_file_to_agent_runs

client = Docent()
agent_runs = convert_nemogym_jsonl_file_to_agent_runs("rollouts.jsonl")
client.add_agent_runs(collection_id, agent_runs)

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More on the conversion process

Each NeMo Gym rollout becomes one Docent AgentRun. At a high level, the converter:
  • turns the rollout input and output into a single Docent transcript
  • maps developer messages to Docent system messages
  • converts NeMo Gym function calls into Docent tool calls and tool messages
  • stores the rollout reward in agent_run.metadata["scores"]["reward"]
  • preserves extra request and response data under agent_run.metadata["source"]
The converter is strict. It expects the rollout to include responses_create_params.input, response.output, agent_ref.name, _ng_task_index, and _ng_rollout_index. responses_create_params.input can be either:
  • a string, which Docent converts into a single user message
  • an array of structured input items
If the rollout uses unsupported message shapes, unsupported content-part types, invalid tool-call wiring, or missing required fields, the converter raises ConversionError instead of trying to approximate the data.