reasoning
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Fine-Tuning 7B Models on Free Colab with GRPO + TRL
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A Colab notebook has been developed to enhance reasoning capabilities in 7B+ models using free Colab sessions with a T4 GPU. By leveraging TRL's comprehensive memory optimizations, the setup significantly reduces memory usage by approximately seven times compared to the naive FP16 approach. This advancement makes it feasible to fine-tune large models without incurring costs, providing an accessible option for those interested in experimenting with advanced machine learning techniques. This matters because it democratizes access to powerful AI tools, enabling more people to engage in AI development and research without financial barriers.
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Rethinking RAG: Dynamic Agent Learning
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Rethinking how agents operate involves shifting from treating retrieval as mere content to viewing it as a structural component of cognition. Current systems often fail because they blend knowledge, reasoning, behavior, and safety into a single flat space, leading to brittle agents that overfit and break easily. By distinguishing between different types of information—such as facts, reasoning approaches, and control measures—agents can evolve to be more adaptable and reliable. This approach allows agents to become simple interfaces that orchestrate capabilities at runtime, enhancing their ability to operate intelligently and flexibly in dynamic environments. This matters because it can lead to more robust and adaptable AI systems that better mimic human-like reasoning and decision-making.
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Benchmarking LLMs on Nonogram Solving
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A benchmark was developed to assess the ability of 23 large language models (LLMs) to solve nonograms, which are grid-based logic puzzles. The evaluation revealed that performance significantly declines as the puzzle size increases from 5×5 to 15×15. Some models resort to generating code for brute-force solutions, while others demonstrate a more human-like reasoning approach by solving puzzles step-by-step. Notably, GPT-5.2 leads the performance leaderboard, and the entire benchmark is open source, allowing for future testing as new models are released. Understanding how LLMs approach problem-solving in logic puzzles can provide insights into their reasoning capabilities and potential applications.
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LLMs Reading Their Own Reasoning
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Many large language models (LLMs) that claim to have reasoning capabilities cannot actually read their own reasoning processes, as indicated by the inability to interpret tags in their outputs. Even when settings are adjusted to show raw LLM output, models like Qwen3 and SmolLM3 fail to recognize these tags, leaving the reasoning invisible to the LLM itself. However, Claude, a different LLM, demonstrates a unique ability to perform hybrid reasoning by using tags, allowing it to read and interpret its reasoning both in current and future responses. This capability highlights the need for more LLMs that can self-assess and utilize their reasoning processes effectively, enhancing their utility and accuracy in complex tasks.