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| 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 | |
| Supplementary release for the paper *Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality* (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents). 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 fourteen 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 alongside the paper *Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality* (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents), to reproduce its reported results. | |
| ## License | |
| Apache License 2.0. | |