Validate the judge reward signal with human calibration and domain-specific evaluators
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 38/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Quiet
- Tech stack
- python
- Domain
- ai, machine-learning
Research direction
Start by locating the SDK’s current reward design, default judges, and learning-core interfaces, then read the whitepaper sections describing intent, adherence, completion, and reweighting. The work is done when a documented calibration workflow, pluggable evaluators, raw per-metric episode values, and a sample human-correlation report are supported without changing the learning core.
Written by the indexing model from the issue text.
Description
The analysis notes that the SDK’s reward model is conceptually clear but still evidence-light: the paper specifies intent, adherence, and completion judges, but does not show that those judges actually correlate with human judgment or domain success. That is a direct risk to the learning loop because reward misspecification can cause the learner to optimize the wrong behavior.
This ticket should turn the current reward design from a plausible architecture into a validated measurement layer.
Suggested scope
Add a reward validation study that measures judge-to-human agreement.
Add support for custom evaluators so teams can plug in domain-specific checks.
Document how to evaluate whether the default judge weights are appropriate for a given workload.
Persist raw per-metric values and make it easy to reweight historical episodes without re-running judges, which the whitepaper already points to as an advantage.
Acceptance criteria
A documented calibration workflow exists for the default judges.
The SDK supports pluggable evaluators without changing the learning core.
A sample report shows how judge scores correlate with human review or task-success outcomes.
- Dominant language
- Python
- Stars
- 10
- Forks
- 9
- Avg merge
- 1d 19h
- Merged PRs (30d)
- 4
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
More from microsoft/agent-learning
-
Difficulty 5/5 Over a week Newbie friendliness 30/100
microsoft/agent-learning#28 ·
-
Difficulty 5/5 Over a week Newbie friendliness 35/100
microsoft/agent-learning#27 ·
-
microsoft/agent-learning#26 · 2 comments · 1 assignee ·
-
Difficulty 5/5 Over a week Newbie friendliness 35/100
microsoft/agent-learning#25 ·
-
Difficulty 5/5 Over a week Newbie friendliness 25/100
microsoft/agent-learning#24 ·
All issues in microsoft/agent-learning
Similar issues
-
area: harness bug status: needs-triage
Difficulty 2/5 1-3 hours Newbie friendliness 75/100
Human-Agent-Society/reef#625 ·
-
Difficulty 2/5 1-3 hours Newbie friendliness 70/100
-
Difficulty 1/5 Under an hour Newbie friendliness 80/100
learningequality/kolibri#15351 · 2 comments ·
-
Difficulty 2/5 1-3 hours Newbie friendliness 75/100
-
Name consistency Open
Difficulty 2/5 1-3 hours Newbie friendliness 75/100
eellak/triplestore#65 · 1 comment ·