by Yun Wu
This short video set is focusing on sharing the papers on GenAI related topic, especially the SOTA (State of the Art) papers that are the foundations of GenAI work. It shows how these researches paved the way to the GenAI tools that we are using every day such as ChatGPT, Gemini, Claude Code etc. This is complementary to https://open.spotify.com/show/7B2L4YDgRdi9LcsdFo9vP3
Language
🇺🇲
Publishing Since
5/18/2026
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August 8, 2026
<p>Title: RL Post-Training Builds Compositional Reasoning Strategies</p><p>Source: http://arxiv.org/abs/2607.07646v1</p><p>Summary:</p><p>This paper provides foundational insights into how reinforcement learning post-training enables models to transition from simple skills to complex, multi-step compositional reasoning strategies. By demonstrating how RL systematically organizes and compresses primitive actions into stable, higher-level reduction procedures, it advances our theoretical understanding of reasoning emergence in advanced generative models.</p>
August 7, 2026
<p>Title: From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents</p><p>Source: http://arxiv.org/abs/2607.07321v1</p><p>Summary:</p><p>This work introduces a novel framework for self-evolving agents by enabling them to autonomously synthesize low-level, atomic actions into reusable, higher-order Standard Operating Procedures (SOPs). This dynamic tool-optimization loop represents a major paradigm shift in reducing reasoning overhead and improving long-horizon task execution for autonomous systems.</p>
August 7, 2026
<p>Title: Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents</p><p>Source: http://arxiv.org/abs/2607.06223v1</p><p>Summary:</p><p>This paper introduces a novel policy optimization framework (IGRPO) that dynamically allocates rollout budgets based on node-level information gain during tree-structured exploration. By unifying adaptive search-tree exploration with a principled reinforcement learning target, it provides a foundational methodology for scaling and training multi-turn reasoning agents.</p>
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This short video set is focusing on sharing the papers on GenAI related topic, especially the SOTA (State of the Art) papers that are the foundations of GenAI work. It shows how these researches paved the way to the GenAI tools that we are using every day such as ChatGPT, Gemini, Claude Code etc.
This is complementary to https://open.spotify.com/show/7B2L4YDgRdi9LcsdFo9vP3
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