A technical framework for implementing test-time compute and chain-of-thought reasoning to improve the accuracy and depth of AI model outputs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install test-time-compute-guide
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install test-time-compute-guide using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Test-time Compute Guide?
The Test-time Compute Guide is a specialized resource for developers looking to implement System 2 thinking in large language models. By utilizing Openclaw Skills, developers can master techniques like sequential revision and process reward models that allow LLMs to spend more computational effort on complex tasks. This approach moves beyond simple prompting, enabling agents to reflect on, verify, and refine their reasoning before producing a final response.
This guide provides the architectural patterns necessary to scale compute at inference time. Whether you are building complex math solvers or logical reasoning agents, integrating these methodologies through Openclaw Skills ensures that your models achieve higher performance by balancing parallel sampling with iterative refinement.
Test-time Compute Guide Use Cases
- Solving complex mathematical or symbolic logic problems requiring multi-step verification.
- Enhancing the reliability of code generation through iterative self-correction.
- Improving research and data synthesis by evaluating multiple reasoning paths simultaneously.
- Implementing high-stakes decision-making agents where process-based verification is required.
How Test-time Compute Guide Works
- Prompting: Employs specific templates to trigger step-by-step reasoning within the model's latent space.
- Parallel Sampling: Generates multiple independent reasoning paths for the same problem using varying temperature settings.
- Evaluation: Uses a Process Reward Model (PRM) or a verification function to score the validity of individual reasoning steps.
- Search & Selection: Executes beam search or Best-of-N selection to identify the path with the highest cumulative reward.
- Final Output: Returns the most logically sound conclusion based on the test-time compute budget allocated.
Test-time Compute Guide Setup
To implement these reasoning techniques within your Openclaw Skills environment, ensure you have Python 3.8+ and the Transformers library installed. Run the following command to set up your environment:
pip install transformers torch
You will also need to define a scoring mechanism or a Process Reward Model (PRM) to effectively evaluate the reasoning steps generated during the inference lifecycle.
Test-time Compute Guide Data Schema & Taxonomy
The skill manages reasoning data and model outputs using the following structure:
| Component | Data Type | Purpose |
|---|---|---|
| CoT Prompt | String | Initiates the step-by-step reasoning process |
| Candidates | List[Tuple] | Stores generated sequences and their associated scores |
| PRM Weights | Model Weights | Evaluates the quality of intermediate reasoning steps |
| Beam State | Object | Tracks cumulative rewards during the search process |
| Temperature Map | Dict | Manages entropy for parallel sampling diversity |
Test-time Compute Guide Advanced Features
- Multi-agent verification where one model reasons and another acts as the process reward judge.
- Dynamic compute scaling that adjusts the number of parallel samples based on problem difficulty.
- Integration with beam search algorithms for guided exploration of reasoning trees.
- Support for self-consistency voting to identify the most frequent answer across multiple rollouts.
SKILL.md
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