Execution-Trace Training for Generative Model Reasoning
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Solution Overview
Problem
Conventional generative models, such as large language models (LLMs), struggle with maintaining cohesive understanding across multiple interactions and often fail to execute sequential reasoning steps accurately, leading to inconsistent performance and flawed reasoning, especially in complex problem-solving tasks.
Innovation Solution
Training generative models on synthetic training data generated by tracing the execution of functions or algorithms, using a Python read-eval-print loop (REPL) to capture step-by-step problem-solving processes, thereby improving their ability to generalize and reason effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional generative models are trained on extensive datasets of pre-existing content, then the model can discern intricate patterns and establish meaningful connections, but the model fails to execute sequential reasoning steps accurately and maintains inconsistent performance
Solution Approach 1:
The patent applies preliminary action by pre-computing execution traces for algorithms and preparing them as structured training data before the generative model needs to perform reasoning tasks. These traces capture the step-by-step execution flow, variable states, and control flow decisions, allowing the model to learn correct sequential reasoning patterns in advance rather than attempting to reason from scratch during inference.
Solution Approach 2:
The patent uses copying by replicating actual algorithm execution traces (including intermediate states, variable values, and control flow) as training examples. Instead of generating synthetic reasoning paths, the model learns by copying and generalizing from real execution traces of algorithms, preserving the faithful representation of correct reasoning processes.
2Adaptability or versatility
If generative models are trained on extensive datasets of pre-existing content, then the model can generate content in accordance with training data, but the model struggles with complex problem-solving tasks requiring multiple interactions
Solution Approach 1:
The patent applies segmentation by breaking down complex algorithm executions into discrete, traceable steps with explicit intermediate states. Each execution trace is segmented into individual operations, variable assignments, and control flow transitions, allowing the generative model to learn and maintain cohesive understanding across multiple reasoning steps through structured, granular training data.
Solution Approach 2:
The patent introduces execution traces as an intermediary representation between raw code and model understanding. These traces serve as a mediator that captures the complete reasoning process including intermediate variable states and control flow, providing the model with a structured bridge to understand complex problem-solving sequences without losing information across interactions.
3Manufacturing precision
If conventional training methods are used with pre-existing content datasets, then the model can learn patterns from training data, but the training data lacks faithful representation of correct reasoning steps
Solution Approach 1:
The patent applies self-service by using algorithms to automatically generate their own execution traces without human intervention. The system executes algorithms, captures their runtime behavior including variable states and control flow, and automatically structures this data for training. This self-generating approach ensures faithful representation of correct reasoning while avoiding the complexity of manual trace creation or external annotation processes.
Data Source
AI summary
A method includes generating, via a virtual machine, a group of code traces, each code trace of the group of code traces corresponding to a respective algorithm, of a group of algorithms, and a corresponding input. The method also includes fine-tuning a generative model in accordance with the group of code traces. The method further includes receiving, at the fine-tuned generative model, computer programming code. The method also includes generating, via the fine-tuned generative mode, one or more computer programming code statements corresponding to the computer programming code or simulate an expected output of the computer programming code.