2.5B-Token Tool-Use Dataset Released with GPT-5.5 Trajectories
By Mr.Xu Community Post
Published:
Summary:A community contributor has published a sanitized dataset of Codex tool-use trajectories on Hugging Face. The post describes coverage of approximately 2.5 billion tokens and 12,121 tool calls, while attributing the underlying activity to GPT-5.5. The dataset could support tool-use modeling, agentic workflow learning, trajectory supervision, and reinforcement learning from real interaction traces. Its practical value nevertheless depends on undocumented details such as the exact model version, t
What the Dataset Contains
A community contributor has published codex-tool-use-trajectories on Hugging Face after applying sanitization to the records. The post reports approximately 2.5 billion tokens and 12,121 tool calls. Its title associates the data with GPT-5.5, but no model-version verification, sampling window, or documentation of the original workload is available. The safest description is therefore a Codex tool-use trajectory dataset attributed by its publisher to GPT-5.5 activity.
Unlike conventional instruction data containing only prompts and responses, tool-use trajectories preserve intermediate agent behavior: tool selection, argument construction, tool results, and subsequent decisions. This makes them closer to Agentic Workflow supervision and potentially useful for teaching a model when to call tools, how to structure arguments, and how to react to returned results.
Why the Tool-Call Count Matters More Than Token Count
A 2.5-billion-token headline does not by itself establish training value. The meaningful unit is a complete stateful decision trajectory, not an isolated function invocation. Important statistics include:
- Tool names and argument structures;
- Success, failure, timeout, and retry rates;
- Tool-output lengths and task-complexity distribution;
- Call depth and context length per task;
- Whether failed calls are followed by useful corrections;
- Duplicate requests, redundant retries, and low-value tool output.
Failed trajectories may still be valuable if they teach error recovery. However, repeated traces or mechanically duplicated workloads can inflate the apparent size of the corpus. Training pipelines should deduplicate at the task, session, or trajectory-cluster level rather than relying only on exact-text matching.
Value for Agentic Workflow Training
The dataset can support several practical experiments:
- Tool-use supervised fine-tuning: Structure states, tool choices, and arguments into training examples.
- Process supervision: Construct action-quality labels from tool feedback instead of fitting only final answers.
- Failure recovery: Retain malformed calls, timeouts, and failed results so the model can learn to diagnose and correct them.
- Trajectory filtering and preference learning: Compare successful and inefficient call chains for the same task.
- Long-context compression: Compress repetitive tool output while preserving the state required by later actions.
If the records use a JSONL structure, common fields may include prompts, model outputs, tool-call lists, tool results, and session identifiers. Before training, engineers should validate schema consistency and normalize tool names, argument types, and result encodings. Concatenating raw trajectories can teach templates, repeated prefixes, or private working habits rather than transferable tool-use behavior.
Privacy and Data-Quality Risks
Sanitization does not automatically make a dataset production-ready. Tool traces may expose filenames, source code, internal URLs, query parameters, terminal output, stack traces, and environment metadata. Removing obvious names may still leave identifiers that can be re-linked to people or organizations. Retrieved web content may also introduce copyright, robots-policy, and retention concerns.
Recommended checks include:
- Scan for API keys, access tokens, passwords, and private keys;
- Search for email addresses, IP addresses, domains, absolute paths, and usernames;
- Measure tool and task-template coverage;
- Quantify exact and near-duplicate rates;
- Validate tool arguments against their schemas;
- Manually verify that call, result, and next-action sequences close correctly;
- Confirm the dataset license and the authorization status of source material before redistribution or commercial use.
Engineering Trade-offs
Large-scale trajectory data does not automatically produce better agents. Full-parameter SFT can rapidly increase the costs of deduplication, context processing, and training. A more practical pipeline uses rule-based filters or lightweight classifiers to remove obvious duplicates, invalid calls, and sensitive records, then samples the remaining data according to tool category and task difficulty.
Long trajectories also introduce a credit-assignment problem. A correct final answer does not imply that every intermediate tool call was necessary or well executed, while one local failure may be recovered successfully later. Practical pipelines can combine deterministic validators, executable tool environments, and outcome-level rewards to relabel trajectories rather than treating every record as an equally valuable positive example.
Recommendations for Developers
Start with a 1,000–10,000-record quality audit covering tool distribution, failure rate, call depth, duplication, and sensitive-information incidence. Run a small-scale tool-use SFT experiment and compare it with a conventional JSON instruction-data baseline. Evaluation should include tool-selection accuracy, executable argument rate, invalid-call rate, average call count, total cost, and final task success.
If the data does originate from GPT-5.5, it could also provide a window into the tool-use behavior of that model. Until an official confirmation or reproducible sampling methodology is available, however, the model-version attribution should be treated as a publisher claim rather than an independently verified fact.
— END —Source: Reddit r/LocalLLaMA (2026-11-08)
Tags: #tool-use dataset #agentic workflow #trajectory supervision #GPT-5.5 #Hugging Face
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