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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.
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