Systems and methods for generative language model reasoning process optimization

A system for training a generative language model (GLM), the system comprising:

• at least one processor;
• and
• at least one memory coupled to at least one processor and configured to store instructions that cause the at least one processor to perform operations, the operations comprising:
• generating, using the GLM, a plurality of sampled rationales for a plurality of question-answer pairs;
• determining, using the plurality of sampled rationales, a gradient estimate of parameters of neurons in the GLM, wherein the gradient estimate maximizes a learning objective of the GLM;
• modifying the parameters of the GLM using the gradient estimate;
• and
• providing a trained GLM with the modified parameters.

A non-transitory computer readable medium, having instructions stored thereon, that when executed by a processor, cause the processor to train a generative language model (GLM), the operations comprising:

• generating, using the GLM, a plurality of sampled rationales for a plurality of question-answer pairs;
• determining, using the plurality of sampled rationales, a gradient estimate of parameters of neurons in the GLM, wherein the gradient estimate maximizes a learning objective of the GLM;
• modifying the parameters of the GLM using the gradient estimate;
• and
• providing a trained GLM with the modified parameters.