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docs: add ABCD synthetic agent conversation dev note Part 2 - #776
Adding Part 2 dev note on ABCD (agent conversation) experiments.
This PR includes the second part of a two post series.
First post describes the ABCD dataset, how to transform the data shape to fit NSS requirements, training/generation, utility and privacy analysis. (PR 774)
Second post includes prompt only (in-context learning), hyper-parameter sweep and privacy/utility tradeoffs, and data augmentation. (This PR)
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New or updated tests for any fix or new behavior
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Summary by CodeRabbit
Documentation
Added a post detailing three experiments with synthetic ABCD conversations, including comparisons of prompt-only generation and fine-tuning, results across 12 configuration settings, and the effect of adding rare-action prefixes.
The post reports methods, metrics, trade-offs, and commands for generating and filtering enriched data.
Updated Part 2 references in the replay-filtering discussion and “Coming Up in Part 2” section to link to the new post.
The pull request adds a post about synthetic ABCD conversation experiments, including measured utility, replay, and rare-action enrichment results. It also updates two Part 2 references in the related post to link to the new post.
Changes
ABCD synthetic-conversation experiments
Layer / File(s)
Summary
Experiment report and related-post links docs/dev-notes/posts/tuning-synthetic-conversations-abcd.md, docs/dev-notes/posts/abcd-conversations-to-synthetic-training-data.md
The new post documents baseline metrics, prompting and configuration results, and rare-action enrichment outcomes. Two Part 2 references in the related post now link to it.
The documentation is mergeable with a bounded reproducibility gap; record the experiment seeds before relying on the reported measurements.
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The title clearly and concisely describes the addition of the ABCD synthetic agent conversation Part 2 documentation post.
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Summary
Adds Part 2 of the ABCD experiment notes and links it from Part 1.
The prompt experiment shows whether examples can replace fine-tuning.
The tuning study maps utility against replay across model settings.
The enrichment study adds rare-action examples with their dialogue context.
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[style] ~118-~118: If you’re making a request, consider inserting ‘please’ to sound more courteous and respectful.
Context: ...rance | | hi! i need to return an item, can you help me with that? | return_size | | []...
[style] ~84-~84: You have already used ‘right’ in nearby sentences. Consider using an alternative word to let your writing stand out and sound more polished.
Context: ...ing the right action and filling in the right values. One-shot retained 14.99% utilit...
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Context: ...ee the dialogue that leads to an action in order to learn when to take it and which values ...
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[style] ~255-~255: Consider a more concise word here.
Context: ...ee the dialogue that leads to an action in order to learn when to take it and which values ...
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Summary
Adding Part 2 dev note on ABCD (agent conversation) experiments.
This PR includes the second part of a two post series.
Some of the reviews were provided in PR 774
Proposal doc
Human review
Human review
Select exactly one. The default is no human review; uncheck it when choosing another level.
Verification performed:
Pre-Review Checklist
Ensure that the following pass:
mise run formatleaves only intended formatting changes.mise run check ::: testpasses locally.mise run test:e2epasses locallymise run test:ci-containerpasses locally (recommended)/syncon this PR to trigger a run (auto-triggers on ready-for-review)Pre-Merge Checklist
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Summary by CodeRabbit