~hackernoon | Bookmarks (9)

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Exploring Classical and Learned Local Search Heuristics for Combinatorial Optimization

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This section delves into the realm of local search heuristics in combinatorial optimization, covering classical heuristics...
This section delves into the realm of local search heuristics in combinatorial optimization, covering classical heuristics and advanced learned heuristics like ECO-DQN and GNN-SAT. It highlights the evolution of machine learning integration in solving CO problems, showcasing the advancements in algorithmic approaches.

deep-learning graph-neural-networks local-search-heuristics neural-networks reinforcement-learning
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Personalized Soups: LLM Alignment Via Parameter Merging - Personalized Human Feedback

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ai-human-feedback large-language-models model-adaptation parameter-merging personalized-alignment
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Personalized Soups: LLM Alignment Via Parameter Merging - Related Work

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ai-human-feedback large-language-models model-adaptation parameter-merging personalized-alignment
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Personalized Soups: LLM Alignment Via Parameter Merging - Abstract & Introduction

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ai-human-feedback large-language-models model-adaptation parameter-merging personalized-alignment
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Comprehensive Coverage: The AI Solution To Unit Testing

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Unit test coverage – the percentage of source code for which unit tests have been written...
Unit test coverage – the percentage of source code for which unit tests have been written - is a measure of how much of an application’s source code is 'touched' by tests. While 100% coverage is often unattainable (because not all code may be suitable for testing), 70% to 80% coverage are considered a robust testing suite. Automated test generation tools can be especially...

ai-in-testing automated-testing code-coverage reinforcement-learning test-automation
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Objective Mismatch in Reinforcement Learning from Human Feedback: Acknowledgments, and References

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Discover the challenges of objective mismatch in RLHF for large language models, affecting the alignment between...
Discover the challenges of objective mismatch in RLHF for large language models, affecting the alignment between reward models and downstream performance. This paper explores the origins, manifestations, and potential solutions to address this issue, connecting insights from NLP and RL literature. Gain insights into fostering better RLHF practices for more effective and user-aligned language models.

llm-optimization llm-research llm-technology llm-training reinforcement-learning
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Objective Mismatch in Reinforcement Learning from Human Feedback: Conclusion

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This conclusion emphasizes the significance of addressing objective mismatch in RLHF methods, outlining a pathway toward...
This conclusion emphasizes the significance of addressing objective mismatch in RLHF methods, outlining a pathway toward enhanced accessibility and reliability for language models. The insights presented indicate a future where mitigating mismatch and aligning with human values can resolve common challenges encountered in state-of-the-art language models, opening doors for improved machine learning methods.

llm-development llm-technology llm-training reinforcement-learning rlhf
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The Iterative Deployment of RLHF in Language Models

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Delve into the complexities of RLHF's iterative deployment, mitigating undesirable language model qualities through exogenous feedback....
Delve into the complexities of RLHF's iterative deployment, mitigating undesirable language model qualities through exogenous feedback. Explore the societal implications and engineering challenges of this approach. Uncover the theoretical alignment of RLHF with contextual bandits, paving the way for potential real-world applications.

llm-development llm-research llm-technology llm-training reinforcement-learning
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Understanding Objective Mismatch

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Delve into the intricate world of objective mismatch in RLHF, driven by three main causes. Investigate...
Delve into the intricate world of objective mismatch in RLHF, driven by three main causes. Investigate the interplay between reward model training, policy model training, and evaluation tools, revealing the challenges in aligning downstream evaluation with reward model scores. Explore ongoing research efforts, from assessing reward model consistency to developing new training methods and datasets, aiming to mitigate the impact of objective mismatch in...

ai-model-training llm-development llm-research llm-training reinforcement-learning
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~hackernoon | Bookmarks (9)

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Exploring Classical and Learned Local Search Heuristics for Combinatorial Optimization

Personalized Soups: LLM Alignment Via Parameter Merging - Personalized Human Feedback

Personalized Soups: LLM Alignment Via Parameter Merging - Related Work

Personalized Soups: LLM Alignment Via Parameter Merging - Abstract & Introduction

Comprehensive Coverage: The AI Solution To Unit Testing

Objective Mismatch in Reinforcement Learning from Human Feedback: Acknowledgments, and References

Objective Mismatch in Reinforcement Learning from Human Feedback: Conclusion

The Iterative Deployment of RLHF in Language Models

Understanding Objective Mismatch