ICPL Baseline Methods: Disagreement Sampling and PrefPPO for Reward Learning | HackerNoon
Briefly

from Hackernoon2 months ago
The disagreement sampling scheme employed in our study enhances the training process by firstly generating a larger batch of trajectory pairs and selectively querying pairs with high variance for reward model updates.
Hackernoonhttps://hackernoon.com/icpl-baseline-methods-disagreement-sampling-and-prefppo-for-reward-learning
In the proxy human preference experiment, we meticulously set limits on human queries, allowing us to effectively measure the human effort involved in the reward learning process.
Hackernoonhttps://hackernoon.com/icpl-baseline-methods-disagreement-sampling-and-prefppo-for-reward-learning
The pseudocode for both reward learning and PrefPPO provides essential methods for implementing our algorithms, showcasing the technical details necessary for replication and further research.
Hackernoonhttps://hackernoon.com/icpl-baseline-methods-disagreement-sampling-and-prefppo-for-reward-learning
Our research highlights the intricate balance between maximizing the efficiency of learning from human feedback while minimizing the burden on human evaluators through strategic sampling.
Hackernoonhttps://hackernoon.com/icpl-baseline-methods-disagreement-sampling-and-prefppo-for-reward-learning
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#reward-learning #human-preference #machine-learning #algorithm-development #research-methodology
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