LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss

Pau Rodriguez*, Michal Klein, Eleonora Gualdoni, Valentino Maiorca, Arno Blaas, Luca Zappella, Marco Cuturi and Xavier Suau*

This software project accompanies the research paper: LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss, NeurIPS 2025 (bibtex).

---

Setup

1. Clone the Repository:

   git clone https://github.com/apple/ml-lineas
   cd ml-lineas
   

2. Install uv

   curl -LsSf https://astral.sh/uv/install.sh | sh
   export PATH="$HOME/.local/bin:$HOME/.cargo/bin:$PATH" # Ensure UV is in PATH
   source ~/.bashrc                                      # Reload the shell configuration
   

3. Install the project/create the environment:

   uv sync
   source .venv/bin/activate
   

4. Download datasets and models. For ease of explanation, we will use the following environment variables to point to where the datasets and models are stored: DATA_DIR and CACHE_DIR. Also, set HF_TOKEN if needed.

   # Required for some specific models like Gemma-2 or datasets like TET
   export HF_TOKEN="your_token"  
   # Optional
   export DATA_DIR="some/path"
   export CACHE_DIR="some/other/path"
   export HF_HUB_CACHE="another/path"
   

   Then call python -m lineas.scripts.download_external_data to download external assets to your local $DATA_DIR. This will download RTP prompts, the Jigsaw toxicity dataset and the COCO captions dataset. Note that models will be downloaded automatically with huggingface. Note you can setup HF_HUB_CACHE to point to a specific folder (see huggingface documentation).

5. Optionally, run the provided tests to make sure the setup is correct. It will download some small models from Huggingface during the first run.

   pytest . -m "not slow"
   

---

Documentation

This repository contains the code for a research paper focusing on controlling model behavior through learned interventions. We provide a pipeline script that enables users to:

1. Extract Activations: Obtain activations from specified model layers.
2. Learn Interventions: Utilize extracted activations to learn interventions that control model behavior.
3. Evaluate Intervened Models: Assess the performance of intervened models on various tasks.

Quick summary of the main files in the repository:

• Python Scripts:
  • pipeline.py: Main pipeline for incremental learning of model interventions.
  • learn_intervention.py: Core functionality for learning interventions from model activations.
• Hydra Configuration Files (configs directory):
  • text_generation.yaml and text_to_image_generation.yaml: Primary config files, specifying:
    • Model architecture and layers
    • Task parameters (e.g., dataset, batch size)
    • Intervention type and settings (e.g., lineas)
    • Evaluation tasks (e.g., RTP, zero-shot evaluation)
  • Referenced Sub-Configs:
    • task_params/fantasy.yaml (task-specific settings)
    • model/gpt2.yaml (model architecture details)
    • intervention_params/lineas (intervention-type specific settings; not explicitly listed, implied as part of the config structure)
    • wandb/lineas.yaml (WandB logging configuration)

The lineas intervention in this repository implements Linear-AcT as defined in our paper: Controlling Language and Diffusion Models by Transporting Activations

Running the pipeline for text generation

# see lineas/configs/text_generation.yaml for configuration details
python -m lineas.scripts.pipeline \
"model=gemma-2-2b" \
"task_params=fantasy" \
"responses.batch_size=32" \
"responses.max_batches=1" \
"wandb.mode=disabled" \
"interventions.batch_size=32" \
"intervention_params=lineas" \
"intervention_params.optimization_params.steps=50" \
"+model.target_module_names=[.*post.*layernorm]" \
"text_generation.num_sentences=10" \
"text_generation.new_seq_len=48" \
"text_generation.strength_sample_size=2" \
"device=cuda" \
"model.dtype=float32"

This command will:

1. Extract activations from a pre-trained Gemma-2-2b model, as specified in configs/text_generation.yaml. We collect 1 batch of size 20 since we provide 20 sentences in data/fantasy.json). Remember to change to device=mps if working on MacOS and to device=cuda if you work on GPU for better speed.
2. Use the responses to learn an intervention. We set intervention_params=lineas and we reduce the steps to 30 to make this example faster, but better performance is achieved with some extra steps (eg. 1000).
3. Generate text with the intervened model. We ask to generate 10 sentences (text_generation.num_sentences=10) at 3 different strengths (text_generation.strength_sample_size=3) between 0 and 1 (so 0.0, 0.5, 1.0).
4. Evaluate the generated text (see evaluations in lineas/configs/task_params/toxicity.yaml and lineas/configs/text_generation.yaml)

Important: responses.batch_size * responses.max_batches sets the number of points that will define the target distribution and it is computed offline. interventions.batch_size sets the number of points that will define the target distribution and it is computed online. Always use interventions.batch_size => 4 if possible.

Note that we use Hydra as configuration and arguments manager.

Results will be stored in results_dir (set in the config file or run with results_dir=<your/results_dir/path>). It will also upload them to wandb if you have set it up. (more about wandb config for this project in configs/wandb/lineas.yaml). For task-specific evaluations (e.g., toxicity, text_generation, zero_shot), modify the evaluation parameter in text_generation.yaml or override it via the command line, and re-run the pipeline.

Running the pipeline for diffusion

While in the paper we optimize for 1000 iterations with learning rate of 1e-5, we have found that 50 iterations and lr of 1e-3 already yields good results for most conditionings. Tested on a single A100 80GB GPU.

python -m lineas.scripts.pipeline \
    --config-name text_to_image_generation \
    task_params=diffusion_prompts \
    'task_params.src_subsets=["none"]' \
    'task_params.dst_subsets=["pixel"]' \
    'task_params.prompt_subset=["none"]' \
    responses.batch_size=4 \
    responses.max_batches=16 \
    interventions.max_batches=null \
    interventions.batch_size=4 \
    wandb.mode=offline \
    'evaluation=["text_to_image_generation"]' \
    text_to_image_generation.batch_size=4 \
    text_to_image_generation.max_batches=2 \
    text_to_image_generation.create_gif=true \
    intervention_params=lineas \
    intervention_params.optimization_params.steps=50 \
    intervention_params.optimization_params.learning_rate=1e-3 \
    intervention_params.optimization_params.optimizer=Adam \
    model=DMD2 \
    model.unet_with_grads=true \
    device=cuda \
    'model.dtype=${dtype:torch.bfloat16}' \
    intervention_params.optimization_params.criterion=wasserstein \
    'model.module_names=["unet.*norm.*"]'

Line by line:

1. --config-name text_to_image_generation chooses the config file in configs/text_to_image_generation.yaml.
2. "task_params=diffusion_prompts" chooses the task diffusion_prompts in configs/task_params
3. "task_params.src_subsets=['none']" and "task_params.dst_subsets=['pixel']" choose the source and destination datasets respectively.
4. "task_params.prompt_subset=['simple_diverse']" chooses the prompt dataset for inference time
5. "responses.batch_size=8" and "responses.max_batches=8" extract 8 responses per batch and run 8 batches. (64 samples). We used 32 source and 32 target prompts in the paper.
6. "interventions.max_batches=null" will use all extrated responses to learn an intervention
7. "evaluation=['text_to_image_generation']" after the intervention, it will generate images. You can also add clip_score here.
8. "text_to_image_generation.create_gif=true" this will save gif animations with the generated images at different strengths. The strengths used are configured in configs/text_to_image_generation.yaml under text_to_image_generation with min_strength, max_strength and strength_steps (actual strengths will be a np.linspace(min_strength, max_strength, strength_steps)).

Results will be stored in results_dir (set in the config file or run with results_dir=<your/results_dir/path>). It will also upload them to wandb if you have set it up. (more about wandb config for this project in configs/wandb/lineas.yaml). In results_dir/generate_with_hooks_diffusion/ you will find the generated images, with a folder for each strength value and guidance scale set up in text_to_image_generation.yaml in the format {strength:.03f}_{guidance:.03f}/<image_id>.png.

---

Running toxicity mitigation

To reproduce experiments related to toxicity mitigation with LLMs we need some additional external data.

# Remember to call the following! 
# Downloads data to /tmp/lineas (or $DATA_DIR if env variable is set)
python lineas/scripts/download_external_data.py

Then, all you need to do is run a pipeline with a toxicity task. Remember to download the model from Huggingface to $CACHE_DIR. The following command runs a toxicity evaluation on qwen2.5-1.5b, with LinEAS trained with 32 data points only.

python -m lineas.scripts.pipeline \
  model=qwen2.5-1.5b \
  task_params=toxicity \
  responses.batch_size=32 \
  interventions.batch_size=32 \
  responses.max_batches=1 \
  intervention_params=lineas \
  intervention_params.optimization_params.steps=1000 \
  +model.target_module_names=[.*post.*layernorm] \
  model.dtype=float32 \
  device=cuda \
  intervention_params.optimization_params.optimizer=SGD \
  intervention_params.optimization_params.learning_rate=0.1 \
  intervention_params.optimization_params.criterion=wasserstein \
  wandb.mode=online wandb.project=lineas-tox # Optional wandb

---

Customizing Hydra Configuration (e.g. text_generation.yaml)

Overview of Configurable Sections

• Model: Specify model architecture, path, and layer names for intervention.
• Task Params: Define task-specific settings (e.g., dataset, batch size).
• Intervention Params: Configure intervention type, incremental mode, and hook parameters.
• Evaluation: Choose evaluation tasks to run after learning interventions.

Example Customizations

1. (preferred) Override Config Values via Command Line:

   • Use key=value pairs, for example:

   python -m act.scripts.pipeline \
       --config-name text_generation \
       interventions.intervention_params.name=your_new_intervention \
       evaluation=[rtp, zero_shot]
   

   • This approach allows for quick testing of different configurations without modifying the YAML file.

2. Change where the intervention is performed:

   The easiest way is to override arguments via commandline model.module_names=['.*layernorm.*]. Another option is to directly modify the config file, e.g,

   model:
     model_path: "path/to/your/model"
     module_names:
       - layer1_regex
       - layer2_regex
   

   or modify/add a new model in configs/model and reference it in text_generation.yaml or text_to_image_generation.yaml.

3. Switch to a Different Intervention:

   interventions:
     intervention_params:
       name: your_intervention_name
       # Update hook_params if necessary for the new intervention
       hook_params:
         key: value
   

4. Modify Evaluation Tasks:

   evaluation:
     - toxicity
     - zero_shot
     # Add or remove tasks as needed
   

---

Cite

@article{rodriguez2025end-to-end,
  title={LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss},
  author={Rodriguez, Pau and Klein, Michal and Gualdoni, Eleonora and Maiorca, Valentino and Blaas, Arno and Zappella, Luca and Cuturi, Marco and Suau, Xavier},
  journal={NeurIPS},
  year={2025}
}