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Found 3 papers, 1 papers with code
Bayesian Reward Models for LLM Alignment
To ensure that large language model (LLM) responses are helpful and non-toxic, we usually fine-tune a reward model on human preference data.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Reward Model Ensembles Help Mitigate Overoptimization
Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger "gold" reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used.
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