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Simpo: Simple preference optimization with a reference-free reward
Abstract Direct Preference Optimization (DPO) is a widely used offline preference
optimization algorithm that reparameterizes reward functions in reinforcement learning from …
optimization algorithm that reparameterizes reward functions in reinforcement learning from …
Interpretable preferences via multi-objective reward modeling and mixture-of-experts
Reinforcement learning from human feedback (RLHF) has emerged as the primary method
for aligning large language models (LLMs) with human preferences. The RLHF process …
for aligning large language models (LLMs) with human preferences. The RLHF process …
Flame: Factuality-aware alignment for large language models
Alignment is a procedure to fine-tune pre-trained large language models (LLMs) to follow
natural language instructions and serve as helpful AI assistants. We have observed …
natural language instructions and serve as helpful AI assistants. We have observed …
Length-controlled alpacaeval: A simple way to debias automatic evaluators
LLM-based auto-annotators have become a key component of the LLM development
process due to their cost-effectiveness and scalability compared to human-based …
process due to their cost-effectiveness and scalability compared to human-based …
Arithmetic control of llms for diverse user preferences: Directional preference alignment with multi-objective rewards
Fine-grained control over large language models (LLMs) remains a significant challenge,
hindering their adaptability to diverse user needs. While Reinforcement Learning from …
hindering their adaptability to diverse user needs. While Reinforcement Learning from …
Length-controlled alpacaeval: A simple debiasing of automatic evaluators
LLM-based auto-annotators have become a key component of the LLM development
process due to their cost-effectiveness and scalability compared to human-based …
process due to their cost-effectiveness and scalability compared to human-based …
Uncertainty-aware reward model: Teaching reward models to know what is unknown
Reward models (RM) play a critical role in aligning generations of large language models
(LLM) to human expectations. However, prevailing RMs fail to capture the stochasticity …
(LLM) to human expectations. However, prevailing RMs fail to capture the stochasticity …
Inform: Mitigating reward hacking in rlhf via information-theoretic reward modeling
Despite the success of reinforcement learning from human feedback (RLHF) in aligning
language models with human values, reward hacking, also termed reward overoptimization …
language models with human values, reward hacking, also termed reward overoptimization …
Self-generated critiques boost reward modeling for language models
Reward modeling is crucial for aligning large language models (LLMs) with human
preferences, especially in reinforcement learning from human feedback (RLHF). However …
preferences, especially in reinforcement learning from human feedback (RLHF). However …
On the algorithmic bias of aligning large language models with rlhf: Preference collapse and matching regularization
Accurately aligning large language models (LLMs) with human preferences is crucial for
informing fair, economically sound, and statistically efficient decision-making processes …
informing fair, economically sound, and statistically efficient decision-making processes …