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dataset_info:
features:
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
- name: metadata
struct:
- name: task_type
dtype: string
- name: description
dtype: string
- name: room_type
dtype: string
- name: difficulty
dtype: string
- name: num_steps
dtype: int64
- name: has_recovery_steps
dtype: bool
- name: has_close_open
dtype: bool
- name: has_admissible
dtype: bool
- name: num_actions
dtype: int64
- name: num_observations
dtype: int64
- name: num_detours
dtype: int64
- name: num_recovery_steps
dtype: int64
- name: has_open_action
dtype: bool
- name: subgoals
sequence: string
- name: total_chars
dtype: int64
- name: trajectory_outcome
dtype: string
- name: failure_reason
dtype: string
- name: type
dtype: string
- name: table_name
dtype: string
- name: sql
dtype: string
- name: label
sequence: string
- name: wrong_sql
dtype: string
- name: wrong_val
dtype: string
- name: correct_val
dtype: string
- name: where_col
dtype: string
- name: error_col
dtype: string
- name: error_reason
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 7988195.684038021
num_examples: 5584
download_size: 1566110
dataset_size: 7988195.684038021
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# mixed-agent-dataset-v6_v2
This dataset is an improved mixed training dataset for AgentBench tasks.
It combines ALFWorld and DBBench with task-specific normalization.
## Source Datasets
- https://huggingface.co/datasets/u-10bei/sft_alfworld_trajectory_dataset_v5
- https://huggingface.co/datasets/u-10bei/dbbench_sft_dataset_react_v4
## Preprocessing
To stabilize multi-task learning, outputs were normalized.
### ALFWorld
Assistant outputs were converted to **action-only format**.
Example:
Before
Think: I should open the drawer
Act: open drawer 1
After
Act: open drawer 1
### DBBench
Assistant responses were normalized to **SQL-only format**.
Example
User: database question
Assistant: SQL query
## Data Cleaning
Duplicate samples were removed using message-level hashing.
messages → canonical string → SHA1 → dedup
## Dataset Purpose
The goal is to improve training stability for agent-style tasks.
Key improvements:
- consistent output formats
- reduced noisy reasoning tokens
- cleaner supervision signal
## Dataset Size
- total samples: ~5k
- split: train
## Intended Use
Supervised fine-tuning (SFT) for LLM agent models in the AgentBench competition. |