Advancing LLM Reasoning Generalists with Preference Trees (2024 · 94 citations)
Open-source LLMs still lag far behind proprietary systems on hard, varied reasoning problems. The authors attribute this to scarce high-quality alignment data and to little study of preference learning for reasoning. They build ULTRAINTERACT, a dataset of difficult math, coding and logic problems drawn from 12…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention (2025 · 66 citations)
Reasoning models get better when they generate longer chains of thought. Standard softmax attention, however, has quadratic cost in sequence length, which makes long outputs expensive. MiniMax-M1 tackles this with an open-weight reasoning model built on a hybrid design. It starts from MiniMax-Text-01, a…
MiniMax-01: Scaling Foundation Models with Lightning Attention (2025 · 50 citations)
Most current LLMs and VLMs handle 32K–256K tokens of context. That is often too little for tasks like reading whole books, working across entire code repositories, or many-shot in-context learning. The main obstacle is that softmax attention cost grows quadratically with length. MiniMax-01 addresses this with a…

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Updated 2 November 2026