TOPReward

TOPReward is a zero-shot reward model that extracts token log-probabilities from an off-the-shelf vision-language model (VLM) as a robotic reward signal. Given a video trajectory and a task instruction, it returns the VLM’s log-likelihood that the instruction is true — no fine-tuning required.

Paper: TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics Project: topreward.github.io Original code: github.com/TOPReward/TOPReward Default backbone: Qwen/Qwen3-VL-8B-Instruct

Overview

TOPReward asks a generic VLM how likely a task instruction is, conditioned on the video of a robot trying to complete that task. Concretely, given:

• A trajectory video (a sequence of frames).
• A task instruction (e.g. “open the drawer”).

it builds a chat prompt of the form

<video>
"The above video shows a robot manipulation trajectory that completes the
 following task: <instruction> Decide whether the above statement is True
 or not. The answer is: True"

forwards it through the VLM, label-masks everything except the very last token, and reads back the log-probability of that token — by default the literal "True" that closes the suffix template. The resulting log P("True" | video + prompt + instruction) is the reward.

Because the method only depends on a frozen VLM, TOPReward is zero-shot: there are no fine-tuned weights to host. The “model” in LeRobot is a small wrapper around transformers’ Qwen3VLForConditionalGeneration plus the label-masking logic. The processor owns the tokeniser and builds the full chat prompt (EO-1/Robometer pattern).

What the LeRobot integration covers

• Standard reward_model.type=topreward configuration through LeRobot.
• VLM loading via the transformers Qwen3VLForConditionalGeneration API.
• Prompt assembly + tokenisation in the processor (matching upstream QwenClient.compute_instruction_reward).
• compute_reward() returns one scalar log-prob per sample.
• LeRobot reward-model save/load — save_pretrained writes only config.json (the VLM is identified by vlm_name).
• An offline labeling script that writes a topreward_progress.parquet (SARM-compatible schema) for RA-BC and overlay.

The current LeRobot port supports the Qwen3-VL client only. Other upstream clients (Gemini, OpenAI, Gemma, Molmo) can be added as follow-up extras.

Installation Requirements

1. Install LeRobot following the Installation Guide.
2. Install the TOPReward optional extra:

pip install -e ".[topreward]"

or, with uv from a source checkout:

uv sync --extra topreward

This pulls in transformers. The first time you run TOPReward, Hugging Face will also download the VLM weights from the Hub (~16 GB for Qwen3-VL-8B-Instruct). A GPU is strongly recommended.

Model Inputs and Outputs

TOPReward expects:

• A trajectory video or sequence of frames.
• A natural-language task description.

In LeRobot datasets the preprocessor reads:

The model returns:

• compute_reward(batch): one log-probability per sample. Higher = better task-video alignment. When success_threshold is finite, returns the binary thresholded value instead.

Usage

Load the reward model directly

from lerobot.rewards.topreward import TOPRewardConfig, TOPRewardModel

cfg = TOPRewardConfig(
    vlm_name="Qwen/Qwen3-VL-8B-Instruct",
    device="cuda",
)
reward_model = TOPRewardModel(cfg)

Use the reward factory

from lerobot.rewards import make_reward_model, make_reward_model_config, make_reward_pre_post_processors

cfg = make_reward_model_config(
    "topreward",
    vlm_name="Qwen/Qwen3-VL-8B-Instruct",
    device="cuda",
    image_key="observation.images.top",
)
reward_model = make_reward_model(cfg)
preprocessor, postprocessor = make_reward_pre_post_processors(cfg)

The preprocessor tokenises the full prompt (video + prefix + instruction suffix), writes Qwen-VL tensors + prompt_length under observation.topreward.*. The model reads those tensors, label-masks based on prompt_length, and extracts the log-prob reward.

Offline dataset labeling

Write a topreward_progress.parquet for RA-BC training and overlay videos:

# Sparse-dense (15 anchors per episode, matches upstream)
uv run python -m lerobot.rewards.topreward.compute_rabc_weights \
    --dataset-repo-id lerobot/libero_10_image \
    --num-samples 15 \
    --device cuda

Then render the progress overlay for any episode:

uv run examples/dataset/create_progress_videos.py \
    --repo-id lerobot/libero_10_image \
    --episode 0 \
    --progress-file topreward_progress.parquet \
    --gif

Configuration Notes

Prompt knobs

The default prompt mirrors the upstream paper:

prompt_prefix = "The above video shows a robot manipulation trajectory that completes the following task: "
prompt_suffix_template = "{instruction} Decide whether the above statement is True or not. The answer is: True"

Both are exposed on TOPRewardConfig for ablation. The suffix template must contain {instruction}.

Chat template

add_chat_template=True wraps the full prompt (including instruction) with the tokenizer’s chat template before tokenisation. Default is False, matching the upstream paper’s main experiments.

Limitations

• The current LeRobot port is inference-only and zero-shot; forward() is not overridden and is_trainable returns False.
• Only the Qwen3-VL family is supported; other upstream clients are out of scope.
• TOPReward inherits the underlying VLM’s biases.

References

• TOPReward project page
• TOPReward paper
• Original TOPReward code
• Qwen3-VL-8B-Instruct

Citation

@article{chen2026topreward,
  title={TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics},
  author={Chen, Shirui and Harrison, Cole and Lee, Ying-Chun and Yang, Angela Jin and
          Ren, Zhongzheng and Ratliff, Lillian J and Duan, Jiafei and Fox, Dieter and
          Krishna, Ranjay},
  journal={arXiv preprint arXiv:2602.19313},
  year={2026}
}

License

The original TOPReward codebase is MIT-licensed. The LeRobot port follows the LeRobot Apache 2.0 license; the wrapped Qwen3-VL weights are subject to the original Qwen license.