Proxy Reward Models for Cold-Start Continuing Text Optimization
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Solution Overview
Problem
Transformer-based machine learning models face challenges with prohibitive computing resources, latency, and unreliable information synthesis in generating continuing texts, especially during cold-start scenarios with new corpora, where engagement data is lacking.
Innovation Solution
A system utilizing a policy model and a reward model, trained with relative preference data and reinforcement learning from human feedback, generates and assesses continuing texts efficiently, leveraging prompt tuning and feedback loops to achieve policy compliance and task success, even in cold-start conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformer-based models generate continuing texts using extensive corpus data, then information synthesis quality improves, but computing resources become prohibitive and latency increases
Solution Approach 1:
The system pre-trains reward models on historical engagement data before they are needed for generating continuing texts. This preliminary action stores learned patterns in model weights, so that during actual text generation, the system can make reliable decisions without re-processing extensive corpus data, thus reducing computing resource consumption while maintaining synthesis quality
Solution Approach 2:
Instead of using the full transformer model for every text generation task, the system creates simplified reward models that copy only the essential decision-making patterns from the trained transformer. These lighter models can quickly assess continuing texts without the computational burden of the original large model, reducing energy usage while preserving reliability
2Reliability
If the system collects extensive engagement data for new corpora during cold-start, then model performance improves, but initialization time and complexity increase
Solution Approach 1:
The reward models are trained to be universal across multiple corpora and domains using diverse historical engagement data. A single reward model can generalize its knowledge to new corpora during cold-start scenarios, eliminating the need to collect extensive new data for each specific corpus and reducing initialization time
Solution Approach 2:
The system implements feedback loops where initial predictions from reward models are quickly evaluated with minimal real engagement data, and the models are rapidly fine-tuned. This feedback mechanism allows the system to achieve good performance with much less data collection time than traditional approaches, as the pre-trained models provide a strong starting point that requires only minor adjustments
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for more efficiently configuring a policy model to generate candidate messages. One of the methods includes prompting a policy model to generate candidate messages for new content being introduced to the system in reference to a control message associated with the new content. A reward model predict a performance of at least one of the candidate messages for the new content against the control message associated with the new content. The candidate messages are tested to obtain actual relative preference data obtained from engagements with the candidates messages being tested. The phantom relative preference data are supplemented with real relative preference data. Candidate messages are selected to send as continuing text.