GRPO

When it comes to reasoning, bigger isn’t always better. Large language models (LLMs) often produce unnecessarily long chains of thought, burning through tokens — and budgets — even for simple problems. While fixed token limits during training can force brevity, they also rob models of the chance to first explore and then compress their reasoning. A new study, Train Long, Think Short, proposes a smarter path: curriculum learning for length control. Instead of a one-size-fits-all cap, the model starts with a generous token budget, learns robust reasoning strategies, and then gradually adapts to shorter limits over time. The result is a model that solves complex tasks with fewer tokens, without losing accuracy. ...

When it comes to language model agents, more minds may not always mean merrier results. Multi-agent reinforcement learning (MARL) promises a flexible path for decomposing and solving complex tasks, but coordinating multiple large language models (LLMs) remains riddled with instability, inefficiency, and memory fragmentation. Enter JoyAgents-R1, a novel framework that proposes an elegant, scalable solution for jointly evolving heterogeneous LLM agents using Group Relative Policy Optimization (GRPO). Developed by researchers at JD.com, JoyAgents-R1 combines memory evolution, policy optimization, and clever sampling strategies to form a resilient multi-agent architecture capable of matching the performance of larger SOTA models with far fewer parameters. ...