@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.
@alex_verem: a team of researchers just proved you don't need a bigger model, you need a smarter plan researchers from Tsinghua and …
Researchers from Tsinghua and South China University of Technology introduced Atomic Task Graph (ATG), a framework that enables 7B-8B open-source models to surpass GPT-4 on complex agent benchmarks without fine-tuning, by using directed graph-based planning and internal simulation to drastically reduce hallucination rates.
Meet GPT-5.6
OpenAI releases the GPT-5.6 model series, delivering more intelligence per token, stronger directional consistency, and introducing programmatic tool calling, sub-agent delegation, and new reasoning tiers, significantly improving autonomous development efficiency.
@FinanceYF5: Everyone is hyping up the million-token context, but a Prime Intellect engineer spoke the truth: GPT-5.5 has 80% retrieval accuracy at 256k, but when extended to one million, it drops to 36%. The model doesn't fail to hold it—it fails to reason over it—the so-called context rot. Why more...
A Prime Intellect engineer pointed out that large language models like GPT-5.5 see retrieval accuracy drop from 80% at 256k tokens to 36% at one million tokens, indicating the 'context rot' problem—the model can accommodate but cannot effectively reason over long contexts, posing a challenge to agent applications.
@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.