Datasets:

kochan13
/

mixed-agent-dataset-v6_v2

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

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.