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Notes on reinforcement learning framework
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ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
The paper introduces ADEPT, a reinforcement learning framework for training multi-fingered robots to perform complex tasks directly from raw visual and tactile perception. The framework involves pre-training a…
SPADE: Self-Play in Adaptive Synthetic Executable Environments
The paper introduces SPADE, a reinforcement learning framework that allows a language model to act as both an Environment Designer and a Reasoning Agent. The Environment Designer generates long-horizon training…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
The paper introduces SkillRise, a reinforcement learning framework that allows large language model agents to learn and reuse skills across related yet distinct tasks. SkillRise improves Pass@1 performance on ALFWorld…
From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation
Researchers introduce a new framework called CARE-PPO to improve language-based quantitative prediction by both accurately estimating numerical values and providing reliable confidence signals. The method uses…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Researchers propose MiniOpt, a reinforcement learning framework that helps large language models solve various optimization problems with limited resources. It breaks down optimization reasoning into structured…
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