NVIDIA-NeMo / NVIDIA-NeMo/Gym

[checkpoint] Restore conversational tool-use simulation trajectories

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area:environment feature
Dominant language
Python
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Merged PRs (30d)
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Description

Parent

Child of #3219 and #3024.

Problem and state owner

resources_servers/conversational_tool_use_simulation/app.py owns the evolving conversation, current actor, pending tool calls, artifacts, failures, terminal state, and judge results in ConversationSessionState.

Proposed adapter

Version and serialize the complete logical trajectory, including phase/actor and pending-call disposition; do not re-run simulator generations already represented in the snapshot.

Implement explicit checkpoint opt-in, route classification, export/restore/retire hooks, execution-to-session binding, revision reporting, atomic replacement under attempt N+1, and stale-attempt fencing.

Acceptance criteria

  • Restore after user, assistant, and pending-tool boundaries selects the exact next actor and never repeats a committed simulator/model operation.
  • Checkpoint after multiple mutations, replace the server process, restore, and compare the next operation with an uninterrupted execution.
  • Corrupt or incomplete bulk state activates no sessions.
  • Attempt N cannot mutate or retire restored attempt N+1.
  • Terminal cleanup is idempotent and releases restored state exactly once.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with resources_servers/conversational_tool_use_simulation/app.py and inspect ConversationSessionState and the existing conversation execution flow. Trace how actors, pending tool calls, artifacts, failures, terminal state, and judge results are mutated; done means the acceptance scenarios pass, including exact next-actor recovery, corrupt-state rejection, stale-attempt fencing, and idempotent cleanup.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
ai
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
35/100

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