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Notes on rl
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STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution
STRETCH uses a dual-loop setup: a Scaffolder generates adaptive challenges near a model’s capability boundary, while a Learner improves solving trajectories with reinforcement learning. The abstract reports experiments…
SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents
Researchers present SkillGate, a method that aims to improve long-horizon agents' ability to select relevant skills during training. The current approach, outcome-rewarded Reinforcement Learning (RL), has limitations…
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
Researchers introduce MADA-RL, a framework that improves reasoning performance in compact models (up to 4 billion parameters) while reducing training cost. This is achieved by specializing the models into generator and…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost, which can…
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Researchers introduce ORCAID, a method for extracting interpretable rule-based policies from reinforcement learning (RL) agents in complex environments with continuous action spaces. The method uses an efficient…
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