Datasets:

Axion1-0
/

axion-training-data

license: apache-2.0
pretty_name: Axion 1.0 Training Data
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
  - question-answering
tags:
  - agents
  - scientific-reasoning
  - tool-use
  - supervised-fine-tuning
  - reinforcement-learning
  - datasets
configs:
  - config_name: default
    default: true
    data_files:
      - split: stage1
        path: data/stage1-*.parquet
      - split: stage2
        path: data/stage2-*.parquet
  - config_name: rl
    data_files:
      - split: train
        path: rl.parquet

Axion 1.0 logo

Axion 1.0 Training Data

Scientific reasoning and tool use for open agent foundation models.

Project Page · Paper · Code

Axion 1.0 follows a three-stage training recipe: learn self-contained reasoning, learn to acquire missing information through tools, then refine these abilities through reinforcement learning. This repository contains the two SFT trajectory splits and the RL training tasks used by the 122B R3 run.

Dataset at a glance

Configuration	Split	Records	Contents
`default`	`stage1`	10,819	Self-contained scientific reasoning; Python execution, without retrieval
`default`	`stage2`	7,544	Verified tool-use trajectories for tasks constructed from public databases
`rl`	`train`	484	RL prompts, reference answers, and task metadata

The two SFT splits contain 18,363 trajectories. Each row is a complete conversation, including its system prompt, reasoning, tool calls, tool responses, and assistant messages. Tool responses are represented within user messages in the released conversation format.

Data construction

Stage I — learn to reason. Model-aware construction strengthens self-contained scientific problems. Solvers can use Python to carry out computations.

Stage II — learn to investigate. Tasks require external information, with reference answers established through structured data and deterministic computation. The released records contain the collected solution trajectories.

See the paper for the construction, verification, training, and evaluation procedures.

Browse the trajectories

Select the default configuration and the stage1 or stage2 split in the Dataset Viewer. The table presents the question and the last assistant response alongside the complete conversation.

Column	Description
`id`	Stable identifier based on the stage and original row position
`question`	First `user` message, copied from the conversation
`response`	Last `assistant` message, retaining its original reasoning and answer tags
`num_messages`	Number of messages, including the system prompt and tool-response messages
`messages`	Complete ordered list of `{role, content}` objects

question and response are browsing columns derived from messages. Use messages to train on the complete multi-turn trajectory.

Load the data

from datasets import load_dataset

repo = "Axion1-0/axion-training-data"

stage1 = load_dataset(repo, name="default", split="stage1")
stage2 = load_dataset(repo, name="default", split="stage2")
rl_tasks = load_dataset(repo, name="rl", split="train")

conversation = stage2[0]["messages"]

The RL configuration has a separate schema: prompt, reward_model, rl_task_category, data_source, and extra_info. It contains tasks and reference answers, rather than completed SFT solution trajectories.

Files

assets/                    Logo and data-construction figure
data/stage1-*.parquet       Stage I SFT trajectories
data/stage2-*.parquet       Stage II SFT trajectories
rl.parquet                 RL training tasks

The repository distributes the complete SFT conversations and RL tasks in Parquet format. The Dataset Viewer and loading examples read these files directly.

Environment-specific runtime path prefixes in a small number of tool outputs have been normalized.

Reference

Axion 1.0: Open Agent Foundation Models, Simplified

Yuqi Jia, Yunjie Tian, Jiayu Zheng, Yijun Liang, Jingchen Sun, Mengyu Zhao, and Di Fu.

Project page

License

This dataset is licensed under the Apache License 2.0.