| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - code-generation |
| - tool-use |
| - agent |
| - knapsack |
| - navigation |
| - rule-diagnosis |
| - codeact |
| - runtime-semantics |
| pretty_name: "Training Traces" |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: knapsack_persistent |
| data_files: |
| - split: train |
| path: data/knapsack/persistent/traces.jsonl |
| default: true |
| - config_name: knapsack_stateless |
| data_files: |
| - split: train |
| path: data/knapsack/stateless/traces.jsonl |
| - config_name: navigation_persistent |
| data_files: |
| - split: train |
| path: data/navigation/persistent/traces.jsonl |
| - config_name: navigation_stateless |
| data_files: |
| - split: train |
| path: data/navigation/stateless/traces.jsonl |
| - config_name: rule_diagnosis_persistent |
| data_files: |
| - split: train |
| path: data/rule_diagnosis/persistent/traces.jsonl |
| - config_name: rule_diagnosis_stateless |
| data_files: |
| - split: train |
| path: data/rule_diagnosis/stateless/traces.jsonl |
| --- |
| |
| # Training Traces |
|
|
| Anonymous supplementary release for a double-blind workshop submission. This dataset |
| holds the teacher agent traces used to fine-tune the paper's LoRA adapters (see the |
| sibling `cap-sweep-eval-data` release and the ten adapter repos alongside this one). |
|
|
| 6,000 agent traces total (1,000 per family x runtime combination), produced by an LLM |
| teacher solving each of the paper's three agentic task families -- Opaque Knapsack, |
| navigation, and rule diagnosis -- under two interpreter runtime conditions. Each config |
| is a persistent/stateless pair for one family; all six were used to fine-tune the |
| correspondingly-named LoRA adapters. |
|
|
| ## Key terms |
|
|
| - **Persistent runtime**: the Python interpreter keeps all variables alive between |
| agent steps. An agent can write `total_weight += w` and it persists to the next turn. |
| - **Stateless runtime**: the interpreter resets after every step; nothing carries over |
| automatically, so the agent must re-establish any state it needs each turn. |
|
|
| ## Format |
|
|
| Each line is a `{"messages": [...]}` chat-format record (system / user / assistant / |
| tool turns) suitable for direct SFT ingestion. Records include the full CodeAct-style |
| system prompt, task prompt, the agent's code blocks and tool outputs (including tool |
| exceptions), through to task completion. |
|
|
| ## Provenance |
|
|
| Released anonymously alongside a NeurIPS workshop submission for reproducibility |
| review. Non-anonymous release (paper citation, full code) will follow after the review |
| process concludes. |
|
|
