kuririrn/alfworld_roundwise_dpo_v1

Dataset Card for ALFWorld Roundwise DPO Pairs

Overview

This dataset provides roundwise preference pairs for Direct Preference Optimization (DPO) built from ALFWorld-style household task trajectories.

Each example corresponds to a single decision round in a multi-turn agent trajectory. The context contains the full conversation history up to the current user observation, and the dataset provides:

• a chosen action: the expert / SFT action at that round
• a rejected action: a behavior that we want the policy to avoid
• a reason field indicating the type of preference

The source trajectories are derived from:

• kuririrn/sft_alfworld_trajectory_dataset_v3to5_admissible_all

Only trajectories without trajectory_outcome = failure are used to construct the DPO pairs.

Data Fields

Each row in the dataset has the following fields:

• messages (list of dicts)

  • Conversational history up to the current decision round.
  • Each entry has keys:
    • role: "system", "user", or "assistant"
    • content: the corresponding text
  • The last message is always a user observation that includes an "Admissible actions" list.

• chosen (str)

  • The expert action for this round, in the format Act: <action string>.

• rejected (str)

  • A counterfactual action for this round, also in the format Act: <action string>, which should be less preferred than chosen in the given context.

• reason (str)

  • Type of preference signal for this pair. One of:
    • "task_succeeded_negative"
    • "implausible_location"
    • "loop_prev_action"

• trajectory_index (int)

  • Index of the original trajectory in the source dataset.

• step_index (int)

  • Index of the decision round within that trajectory.

• metadata (dict or str)

  • Original metadata from the source trajectory (for example: task type, room type, difficulty).

• dataset_version (str)

  • Version tag of the source dataset (e.g., "v3", "v4", "v5").

• trajectory_outcome (str)

  • Outcome label from the source trajectory. Trajectories with failure labels are excluded when constructing this DPO dataset.

Preference Types (reason)

1. task_succeeded_negative

This reason targets premature success:

• chosen: the expert action for the current round
• rejected: always Act: task succeeded

Even when the task has not yet been completed, models sometimes output "Act: task succeeded". This preference type teaches the model to avoid declaring success too early and to keep executing meaningful actions instead.

2. implausible_location

This reason targets semantically implausible search locations for the goal object.

• chosen: the expert action for the current round
• rejected: an action such as go to <location> (or open <location>) that is implausible given the goal object.

Plausible and implausible locations are determined using co-occurrence statistics between objects and locations computed from the entire ALFWorld SFT dataset. Locations whose relative frequency for a given object is below a certain threshold are treated as implausible. For each decision round, all such implausible actions present in the admissible action list are turned into negative samples.

This encourages the model to prefer reasonable search locations (e.g., fridge or cabinet for food) over clearly unrelated ones (e.g., garbagecan, toilet) when looking for a particular object.

3. loop_prev_action

This reason targets unproductive action loops, focusing on use-type actions.

In the AgentBench setting, actions like use desklamp 1 can be repeated many times without making actual progress, leading to loops and timeouts.

For each decision round:

• chosen: the expert action at the round
• rejected: every admissible use ... action that is different from chosen

This teaches the model that, in contexts where the expert chooses a meaningful move (such as moving to another receptacle or interacting with objects), repeatedly executing nearby use actions is less preferred.

Note that examine actions are not treated as loop candidates here, since they are often necessary as exploratory actions and are heavily used productively in successful trajectories.

How the Pairs Are Constructed

1. Start from kuririrn/sft_alfworld_trajectory_dataset_v3to5_admissible_all.
2. Exclude trajectories with explicit failure outcomes (trajectory_outcome).
3. For each remaining trajectory, process it round by round:
   • Extract the goal object and (if available) goal container from the task description.
   • For each user observation that lists admissible actions:
     • Parse the next assistant action as the chosen action.
     • Enumerate possible rejected actions based on the three reasons above.
4. Collect all (messages, chosen, rejected, reason, ...) as raw DPO pairs.
5. Optionally, rebalance the dataset by sampling so that the final dataset maintains a desired ratio between the three reason categories.

The final dataset thus contains roundwise preference pairs that:

• discourage premature success declarations,
• penalize clearly implausible search locations, and
• reduce unproductive use loops.

Splits

The dataset is provided as a DatasetDict with two splits:

• train: for training DPO or preference-based finetuning.
• validation: for monitoring overfitting and verifying stability.

The split is created by shuffling the balanced DPO examples with a fixed random seed and performing a simple train/validation split (for example, 95% / 5%).

Intended Use

This dataset is intended to be used for:

• Direct Preference Optimization (DPO) or similar preference-based finetuning methods on ALFWorld-style agent models.
• Research on how explicit preference signals about task success, search strategies, and action loops affect multi-step agent behavior.

Citation

If you use this dataset in academic work, please consider citing the original ALFWorld environment and the dataset author:

• ALFWorld: "ALFWorld: Aligning Text and Embodied Environments for Interactive Learning" (Shridhar et al., 2020)
• Dataset author: kuririrn (Hugging Face)