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get grpo run

get_grpo_run
Read-onlyIdempotent

Fetch a GRPO run's live status, ledger-true spend, and training outcome (reward trend, bake-off verdict) to monitor a run you launched.

Instructions

Fetch one online-RL (GRPO) run's status, ledger-true spend, and training outcome (reward trend, bake-off verdict) — use it to poll a run you started. GET /v1/grpo/runs/{id} (API-key scope: read). Returns: JSON object: { id, status, model, budget_usd, spent_usd, gpu_spent_usd, env_spent_usd, gpu_hour_budget, gpu_rate_usd_per_hour, created_at, outcome: null | { steps, first_half_mean_reward, second_half_mean_reward, stopped_by_tripwire, bakeoff?: { verdict, delta, delta_ci95, prompts, k, mean_sim_fraction, mean_tool_steps } } }. Outcome is always attempted for this single run (null while ACTIVE or when no artifact exists). Notes: Feature-flag gated (fineTuning flag off → 404). OWNER/ADMIN key required (403). 404 'Run not found' for foreign ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe GRPO run id (from the runs list). Scoped to the workspace: a foreign or unknown id is 404.
Install Server

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses key behaviors: the exact response shape, outcome being null while ACTIVE or without artifact, feature-flag gating causing 404, OWNER/ADMIN key requirement causing 403, and foreign-id behavior. This is substantial context that helps the agent anticipate failures and interpret results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place: purpose and usage first, followed by the HTTP endpoint and auth note, then a full return payload since no output schema exists, then edge-case notes. It is dense but well-structured and free of filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter, no output schema, and rich annotations, the description is complete: it specifies the endpoint, auth/scope requirements, response object, null semantics for outcome, and error cases. An agent has all necessary information to call and interpret this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully documents the single 'id' parameter, including its source ('from the runs list') and scoping behavior. The description adds no additional parameter-level meaning beyond what the schema provides, so the high schema coverage baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Fetch') and resource ('one online-RL (GRPO) run') and enumerates exactly what is returned: status, spend, and training outcome. This clearly distinguishes it from sibling tools like list_grpo_runs and get_grpo_run_weights.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use it to poll a run you started, which establishes the primary usage context. It does not explicitly name alternatives or say when not to use it, but the single-run scope and purpose make the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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