Olive Song

Why AI Agents Need Million-Token Context — Thomas Wolf & Olive Song, MiniMax

Sep 4, 2026 · 20:48

MiniMax's M3 pairs a functional one-million-token context window with coding and multimodal abilities in a 400-billion-parameter, 20-billion-activated model, explain Olive Song, RL Lead at MiniMax, and Thomas Wolf, co-founder of Hugging Face. Song says short context fails when agents handle multi-round tool responses; MiniMax Sparse Attention — an index branch selecting what matters plus a sparse branch computing on selected blocks — was designed by an intern. She argues multimodal training from the very first step beats post-hoc adapters that harm text performance and risk collapse; interleaved data and reward modeling solved this. Its apps reach over 300 million people in 200 countries; anyone can propose projects, and community feedback and internal agent harnesses now drive M3.1.

Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It — Dan Fu and Olive Song

Jul 31, 2026 · 20:14

Olive Song, RL lead at MiniMax, and Dan, Together AI's VP of Kernels, argue that open-weight MiniMax M3 is closing the gap to frontier labs via community feedback and inference optimization. Dan details the day-zero stack: GPU kernels for M3's sparse attention, KV cache at million-token contexts, and adapting to agentic workloads that upload codebases. Song says M3 trains multimodal from scratch to prevent text-vision collapse, and uses RL with environment design and rewards for long-horizon tasks like replicating a 12-hour iClear paper, plus validation/test splits to avoid hacking. Together's Parallel Kernel Bench holds unsolved problems, so overfitting it yields kernels the company deploys. Both see self-evolution and better GPU utilization as why open models keep accelerating.

Minimax M2: Building the #1 Open Model – Olive Song, MiniMax

Dec 13, 2025 · 13:41

Olive Song, Senior Researcher at MiniMax, presents the Minimax M2, an open-weight model with 10 billion active parameters that achieves top rankings in intelligence and agentic benchmarks while being cost-efficient for real-world coding tasks. The model's success is attributed to four key characteristics: scaled environments and expert developer feedback for code experience, interleaved thinking with reinforcement learning for long-horizon tasks, perturbation pipelines for robust generalization across agent scaffolds, and small size enabling multi-agent scalability. Song details how these features allow M2 to handle noisy, dynamic environments, perform multi-tool workflows autonomously, and adapt to various scaffolds and prompts. The talk also previews future developments like M2.1 and M3, with plans to integrate audio and video generation capabilities.