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How to Build Efficient Agentic Reasoning Systems by Dynamically Pruning Multiple Chain-of-Thought Paths Without Losing Accuracy

In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple reasoning paths in parallel and dynamically reduces them using consensus signals and early stopping. We focus on improving reasoning efficiency by reducing unnecessary token usage while preserving ...

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A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a ...

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