@berryxia: Small model, big wisdom? It's now real! A 7B small model now acts as the boss of top large models like GPT-5, Claude Sonnet 4, Gemini 2.5 Pro. A new paper shows an RL-trained 7B model learned to write natural language subtasks, assign them to different models, precisely...
Summary
A new paper proposes training a 7B small model via reinforcement learning as a task scheduler, automatically decomposing subtasks and assigning them to top models like GPT-5 and Claude. It surpasses individual frontier models on several hard benchmarks, demonstrating that end-to-end reward learning can effectively replace manual prompt engineering and multi-agent pipeline design.
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@mylifcc: This is not an ordinary large model, but a Multi-Agent Orchestration System—a small model itself that intelligently and dynamically coordinates multiple cutting-edge models such as GPT, Claude, and Gemini, autonomously assigning roles, decomposing tasks, and completing comp...
Sakana AI has released a Multi-Agent Orchestration System that uses a small model to intelligently coordinate cutting-edge large models like GPT, Claude, and Gemini to autonomously assign tasks and handle complex workloads.
@cuisitekp: A 9B model outperforms models several times larger. The team behind OLMo/Tülu from Ai2 and the University of Washington released a new paper called Tmax, claiming it's the strongest open-source RL training recipe for 'terminal agents'. Result: A 9B model on Terminal-Be…
Ai2 and the University of Washington released a paper titled Tmax, proposing the strongest open-source terminal agent RL training recipe to date. A 9B parameter model outperforms larger models on Terminal-Bench 2.0, with the key being low-cost generation of vast amounts of verifiable training data, not model size or algorithm.
@AYi_AInotes: Everyone is raving about Japan's Fugu beating GPT on benchmarks, but I bet 99% of people haven't understood what really makes it mind-blowing. First off, this isn't some giant monolithic model at all—it has only 0.6B parameters and essentially works as an AI project manager. It handles simple tasks on its own, automatically splits complex ones, and selects the most suitable models from a global pool of top-tier models...
Sakana AI releases Fugu, a multi-agent orchestration system with only 0.6B parameters. By intelligently splitting tasks and coordinating multiple models, it achieves state-of-the-art performance while bypassing traditional parameter scaling. This marks the transition of multi-agent orchestration from a lab curiosity to a practical productivity tool.
@snowboat84: Have you noticed that the birth of models in AI is actually quite arbitrary? Take language models as an example: first RNN, then LSTM, one day Transformer is said to be effective so everyone switches to it, later it's split into Encoder and Decoder, one moment BERT is all the rage, the next GPT is said to have emergent abilities and Scaling Law. The whole process hardly has any theoretical guidance.
The article discusses the arbitrariness of AI model creation, proposing to draw inspiration from physics models, build a repository of candidate models, and formalize the model selection process.
@Gracker_Gao: AI Papers: Strong AI Doesn't Write Code by Writing Code Two recent arXiv papers reveal a counterintuitive finding: when encountering an unfamiliar programming language, GPT-5.4 and Claude Opus 4.6 don't directly write code in the target language—instead, they write a Python program to generate the target code, then debug it locally. This "meta-…
Two recent arXiv papers found that GPT-5.4 and Claude Opus 4.6 employ a metaprogramming strategy when handling unfamiliar programming languages — generating target code with Python and debugging locally — rather than writing the target language code directly. This strategy is key to distinguishing top-tier agents from average ones, and strategy sophistication matters more than model parameter scale.