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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

TOOL · arXiv cs.AI English(EN) · 15h

Not All Transitions Matter: Evidence from PPO

Researchers have developed a method to improve the stability of reinforcement learning training by randomly dropping a fraction of transitions from on-policy rollouts. This technique, applied to Proximal Policy Optimization (PPO), breaks the repetitive gradient structure caused by causally chained states. By dropping approximately 25% of transitions, the method maintains reward performance while yielding more consistent training dynamics across various metrics. AI

IMPACT Enhances training stability for reinforcement learning agents, potentially leading to more reliable and efficient development of AI systems in complex environments.
TOOL · arXiv cs.LG English(EN) · 4d

Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO

Researchers have developed a new architecture called Target Decoupling to address issues in multi-timescale reinforcement learning. This approach separates short-term and long-term signals to improve policy updates, preventing common problems like surrogate objective hacking and policy collapse. Experiments on the LunarLander-v2 environment showed significant performance gains and reduced variance compared to existing methods. AI

IMPACT Introduces a novel architecture that enhances performance and stability in reinforcement learning tasks.