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Why The ‘All-In’ Bet Could Revolutionize In-Game Economy Systems - Game Wisdom
Traditional in-game economies focus mainly on the monetization of assets while promoting engagement. However, by adopting poker’s all-in strategy and incorporating it into game economy, studios can strike a new balance and create more exciting games. Players who have the option to play “all-in” must make decisions, considering their willingness to risk it all and start over or win big.
Memorization vs. Generalization: How Supervised Fine-Tuning SFT and Reinforcement Learning RL Shape Foundation Model Learning - MarkTechPost
Modern AI systems rely heavily on post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL) to adapt foundation models for specific tasks. However, a critical question remains unresolved: do these methods help models memorize training data or generalize to new scenarios? This distinction is vital for building robust AI systems capable of handling real-world variability. Reference: https://arxiv.org/pdf/2501.17161 Prior work suggests SFT risks overfitting to training data, making models brittle when faced with new task variants. For example, an SFT-tuned model might excel at arithmetic problems using specific card values (e.g., treating ‘J’ as 11) but fail if the rules
Curiosity-Driven Reinforcement Learning from Human Feedback CD-RLHF: An AI Framework that Mitigates the Diversity Alignment Trade-off In Language Models - MarkTechPost
Large Language Models (LLMs) have become increasingly reliant on Reinforcement Learning from Human Feedback (RLHF) for fine-tuning across various applications, including code generation, mathematical reasoning, and dialogue assistance. However, a significant challenge has emerged in the form of reduced output diversity when using RLHF. Research has identified a critical trade-off between alignment quality and output diversity in RLHF-trained models. When these models align highly with desired objectives, they show limited output variability. This limitation poses concerns for creative open-ended tasks such as story generation, data synthesis, and red-teaming, where diverse outputs are essential for effective performance. Existing approaches to LLM
Can AI Understand Subtext? A New AI Approach to Natural Language Inference - MarkTechPost
Understanding implicit meaning is a fundamental aspect of human communication. Yet, current Natural Language Inference (NLI) models struggle to recognize implied entailments—statements that are logically inferred but not explicitly stated. Most current NLI datasets are focused on explicit entailments, making the models insufficiently equipped to deal with scenarios where meaning is indirectly expressed. This limitation bars the development of applications such as conversational AI, summarization, and context-sensitive decision-making, where the ability to infer unspoken implications is crucial. To mitigate this shortcoming, a dataset and approach that systematically incorporates implied entailments in NLI tasks are needed. Current NLI benchmarks like SNLI,