# Research Task: Autonomous Weight-Space Acceleration for π₀.₅

## Background

We currently have two trained VLA policies:

1. π₀.₅ Latest
2. π₀.₅ Training-Time RTC

Both policies can already perform some manipulation tasks on a real robot, but there is still room to improve their execution speed.

I would like to explore the **Weight-Space Programming** approach recently proposed by MIT: having a frontier model directly understand and modify neural network weights to improve a VLA policy's real-world execution speed without conventional gradient-based training.

Reference:

https://rikiyat.github.io/blog/weight-space-programming/

This is an exploratory Weight-Space Programming research project, rather than a conventional inference acceleration, runtime optimization, or fine-tuning task.

**The core goal is to make the robot's actions noticeably faster while preserving the original policy's motion smoothness, stability, and task success rate.**

The motion does not need to be smoother than the original, but the speed improvement must not introduce noticeable stuttering, shaking, abrupt stops, or discontinuities.

## Objective

**Can we directly accelerate a pretrained VLA policy through weight-space editing, without gradient-based training, while preserving its original motion smoothness and task performance?**

The aim is to make the policy generate more efficient action trajectories by directly analyzing and modifying π₀.₅'s weights.

Specific goals:

1. **Faster Execution**: Significantly reduce the time the real robot needs to complete a task.
2. **Preserved Smoothness**: Keep motion smoothness at least close to that of the original policy, without noticeably increasing stuttering, shaking, or action discontinuities.
3. **Preserved Success Rate**: Preserve or improve the original task success rate as much as possible.
4. **Minimal Weight Modification**: Modify as few parameters as possible.
5. **No Runtime Overhead**: Introduce no additional inference modules or deployment latency.
6. **Generalization**: Investigate whether the acceleration generalizes across tasks, initial states, and object positions.

Prioritize improvements in actual robot execution speed over simply changing action magnitudes.

## Phase 1: Autonomous Investigation

Begin by independently exploring the current codebase, model architecture, and experiment records.

Locate:

- The model weights and configuration for π₀.₅ Latest.
- The model weights and configuration for π₀.₅ Training-Time RTC.
- Recent real-robot execution logs.
- The corresponding robot states, action trajectories, execution times, task success rates, and videos.
- The current policy's inference pipeline, action expert architecture, and flow matching implementation.
- The current runtime's action chunking, RTC, action interpolation, smoothing, and execution mechanisms.

Prioritize Training-Time RTC.

Focus on the following questions:

- Which stages account for most of the current policy's task execution time?
- Are there unnecessary pauses, waits, or periods of slow motion?
- Are there action paths that could be shortened?
- Are there velocity discontinuities between action chunks?
- Is execution speed primarily limited by the policy's predictions or by the runtime/controller?
- Is the current policy already close to the hardware's safe motion speed limits?
- Can execution efficiency be improved by changing the distribution of actions produced by the policy?

Pay particular attention to the following:

**Do not equate inference latency reduction with robot execution acceleration.**

Clearly distinguish between:

1. Model Inference Latency
2. Action Chunk Generation
3. Runtime Scheduling / Execution
4. Robot Motion Duration
5. End-to-end Task Completion Time

Our primary goal is to improve items 4 and 5.

If the main bottleneck lies in the runtime, report that honestly. However, this study should still prioritize exploring the potential of weight-space editing.

## Phase 2: Safe Model Duplication

Before running any experiments:

1. Locate the original Training-Time RTC checkpoint.
2. Create a separate experimental copy.
3. Record the original checkpoint's hash, configuration, and version.
4. Apply all weight edits only to the experimental copy.
5. Establish mechanisms for saving, loading, comparing, and rolling back weight patches.
6. Write all new experiment outputs to separate directories.

**Never overwrite, delete, or modify any original model weights, configurations, experiment records, or data.**

Do not modify π₀.₅ Latest for now.

Consider transferring the approach to Latest only after making clear progress on Training-Time RTC.

If loading the model could write to the original checkpoint, eliminate that risk first.

## Phase 3: Weight-Space Investigation

Follow the research approach described in MIT's Weight-Space Programming work:

**Inspect → Hypothesize → Edit → Evaluate → Iterate**

You may independently design analysis tools, including:

- Weight Inspection
- Activation Inspection
- Action Expert Investigation
- Flow Matching Trajectory Analysis
- Layer-wise Sensitivity Analysis
- Sparse Weight Editing
- Low-rank Weight Editing
- Forward-only Parameter Search
- Action Distribution Analysis
- Motion Smoothness Analysis

Focus on the following questions:

### 3.1 Can we identify speed-related directions in weight space?

Can we identify parameter directions in the action expert or other relevant modules that affect the robot's motion speed?

For example:

- Do certain parameters affect action magnitudes?
- Do certain parameters affect slow phases of a trajectory?
- Do certain parameters affect the temporal distribution of action chunks?
- Do certain parameters affect acceleration and deceleration behavior?

Try to identify weight-space directions that change execution speed while preserving the action targets and overall trajectory shape as much as possible.

### 3.2 Can we accelerate motion without introducing jerk?

Can localized weight edits make the policy generate faster trajectories that remain continuous?

We would like to achieve:

\[
T_{\mathrm{edited}} < T_{\mathrm{original}}
\]

while satisfying:

\[
J_{\mathrm{edited}}
\approx
J_{\mathrm{original}}
\]

where:

- \(T\) denotes actual task completion time.
- \(J\) denotes trajectory jerk or another measure of motion smoothness.

Note that the absolute value of jerk depends on motion speed and timescale, so raw jerk values alone cannot determine whether motion is smoother.

Evaluate velocity, acceleration, jerk, action discontinuities, and actual execution behavior together.

### 3.3 Can we preserve task semantics while accelerating execution?

The policy should still complete the original manipulation tasks correctly.

For example:

- Grasp locations must not shift noticeably as a result of acceleration.
- The robot must still decelerate appropriately when approaching a target.
- Contact with objects must not cause pronounced impacts.
- The timing of gripper opening and closing must remain appropriate.
- Task completion detection must not trigger prematurely.

**We are aiming for more efficient motion, rather than simply multiplying every action by a factor greater than 1.**

## Phase 4: Autonomous Weight Editing

Independently perform weight-space editing on the separate experimental copy of Training-Time RTC.

You may explore different approaches, including:

- Sparse Parameter Modification
- Low-rank Weight Updates
- Selected Layer Editing
- Action Expert Weight Surgery
- Behavior-related Circuit Editing
- Forward-only Weight-Space Search

You do not need to commit to a particular method in advance.

You may propose new editing strategies based on the model architecture, weight distributions, and experimental results.

However, you must follow this restriction:

**Do not use backpropagation, gradient descent, optimizer steps, LoRA fine-tuning, or any other gradient-based training methods.**

You may:

- Read model parameters.
- Analyze activations.
- Run forward passes.
- Compare the action outputs of different weight patches.
- Use gradient-free search methods.
- Iterate independently based on experimental feedback.

For every edit, record:

1. The layers/modules modified.
2. The number and percentage of parameters modified.
3. The exact form of the weight patch.
4. Action predictions before and after the edit.
5. Velocity, acceleration, and jerk before and after the edit.
6. Estimated task completion time before and after the edit.
7. Potential effects on task success rate and motion stability.

Do not assume that any particular weight-editing method will work.

Failure is acceptable; use failed experiments to adjust the research direction.

## Phase 5: Evaluation

Prioritize validation using existing experimental records and offline data.

If a simulation environment is available, you may also perform closed-loop evaluation.

Do not automatically start real-robot execution without my explicit confirmation.

### 5.1 Baselines

Compare at least the following:

1. Original Training-Time RTC
2. Original Policy + Action Scaling
3. Original Policy + Trajectory Time Scaling
4. Random Weight Editing
5. Proposed Weight-Space Editing

Action Scaling and Trajectory Time Scaling are only control experiments and must not be presented as Weight-Space Programming.

You may also independently add other reasonable baselines for speed optimization.

### 5.2 Metrics

**Execution Efficiency**

- End-to-end Task Completion Time
- Robot Motion Duration
- Average EEF Velocity
- Peak EEF Velocity
- Idle / Pause Duration
- Trajectory Length

**Motion Smoothness**

- Velocity Continuity
- Acceleration Continuity
- Jerk
- Action Chunk Boundary Discontinuity
- Oscillation Frequency
- Unexpected Stops

**Task Performance**

- Task Success Rate
- Final Position Error
- Grasp / Placement Accuracy
- Collision Rate
- Contact Stability

**Weight Editing**

- Number of Modified Parameters
- Percentage of Modified Parameters
- Weight Patch Norm
- Inference Latency
- Regression on Other Tasks

### 5.3 Success Criteria

Prioritize exploring whether the following can be achieved:

- Reduce task completion time by **20%–30%**.
- Keep the task success rate at least as high as the original, allowing for statistical uncertainty.
- Maintain motion smoothness close to the original.
- Introduce no noticeable new stuttering, oscillations, or unexpected stops.
- Do not increase model inference latency.
- Require no additional runtime controller.

20%–30% is an exploratory target and must not be treated as a mandatory requirement.

Even if the result is only a stable 5%–10% speedup, retain it as long as the improvement can be shown to come from weight editing and there is no noticeable performance degradation.

Without sufficient closed-loop evaluation, do not directly interpret improvements in offline action predictions as improvements in real execution speed.

## Phase 6: Weight-Space Speed Control

If the preceding experiments make progress, you can further explore a question with greater research value:

**Can we discover a controllable speed direction in the weight space of a pretrained VLA?**

Suppose there is a parameter direction:

\[
\Delta\theta_{\mathrm{speed}}
\]

Can we then use:

\[
\theta'=\theta+\alpha\Delta\theta_{\mathrm{speed}}
\]

to control the policy's motion speed?

For example:

| α | Expected Behavior |
|---|---|
| 0 | Original Policy |
| 0.25 | Slightly Faster |
| 0.5 | Moderately Faster |
| 1.0 | Significantly Faster |

Validate the following:

1. Does speed vary approximately monotonically with α?
2. Do different values of α preserve similar motion trajectories?
3. Is there a noticeable trade-off between speed and smoothness?
4. Can this direction generalize to different tasks?
5. Is there a safe and stable range of α?

Finding a controllable, reusable speed direction would have greater research value than simply obtaining a faster checkpoint.

## Research Principles

This is an open-ended autonomous research task.

You may independently:

- Read the code and model architecture.
- Analyze weights and activations.
- Write experimental scripts.
- Run model inference.
- Construct and test weight patches.
- Design new diagnostic experiments.
- Revise research hypotheses.
- Try different weight-space editing methods.

You do not need to ask me for confirmation at every step.

However, you must ensure the following:

1. Keep the original checkpoint and experimental data completely safe.
2. Do not use gradient-based training.
3. Do not disguise runtime optimization as weight editing.
4. Do not disguise simple action scaling as a new research method.
5. Do not sacrifice task success rate or motion stability to improve speed.
6. Do not start a real robot without authorization.
7. All conclusions must be supported by reproducible experimental evidence.

Pay particular attention to the following:

**Greater action magnitudes do not mean faster task completion.**

If the robot's actuators have speed limits, control delays, or action saturation, simply scaling up actions may lead to more severe oscillations and task failures.

You must assess whether the speed improvement holds by examining the actual execution pipeline.

## Expected Deliverables

The desired final deliverables are:

1. A separate Training-Time RTC checkpoint with edited weights.
2. The corresponding weight patch file and reproducible code.
3. A comparison of action trajectories from the original and modified policies.
4. Evaluation results for execution speed, motion smoothness, and task performance.
5. Research conclusions on whether weight-space acceleration is effective.
6. If it is effective, an analysis of which parameter modifications caused the speed changes.
7. If it is ineffective, an analysis of the technical bottlenecks and directions worth exploring next.
8. If a controllable speed direction is discovered, the corresponding parameter patch and control experiments.

Do not make parameter modifications unsupported by evidence merely to deliver a modified checkpoint. If no effective result can be obtained, report that honestly.

## Final Instruction

**Start with Training-Time RTC.**

Autonomously investigate the model architecture, weights, action generation process, and existing execution logs.

Explore whether direct weight-space editing can produce faster robotic motion while preserving the original policy's smoothness and task performance.

Preserve all original artifacts.

Do not overwrite any existing checkpoint.

Do not use gradient-based training.

Do not modify the production runtime or robot controller.

Iterate based on evidence, and prioritize measurable improvements over speculative claims.

**The ultimate goal is not merely to make the robot move faster, but to discover whether execution speed can be directly programmed into the weight space of a pretrained VLA.**