Scaling Up Good Synthetic Reasoning We're hiring! ➡️ https://xitter.cf/t.co/Gqtk6KrIy6 / DM 💬 @NathanThinks

Joined May 2022
Releasing Big-MATH—the first heavily curated & verifiable dataset designed specifically for large-scale RL training & LLM reasoning! 📝 250,000+ problems, 47k NEW Q's ✅ 10x larger than existing datasets like MATH 🧑‍⚖️ Verifiable—we eliminated 400k+ problems Details below! 🧵👇
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we’re at #COLM2025🍁 come see our poster # 26 (session 1) today reach out ✉️ if you'd like to chat!
Qwen+RL = dramatic, Aha! Llama+RL = quick plateau Same size. Same RL. Why? Qwen naturally exhibits cognitive behaviors that Llama doesn't Prime Llama with 4 synthetic reasoning patterns & it matched Qwen's self-improvement performance! We can engineer this into any model! 👇
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SynthLabs retweeted
the future is about smart tokens
What if models could learn which problems _deserve_ deep thinking? No labels. Just let the model discover difficulty through its own performance during training. Instead of burning compute 🔥💸 on trivial problems, it allocates 5x more on problems that actually need it ↓
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What if models could learn which problems _deserve_ deep thinking? No labels. Just let the model discover difficulty through its own performance during training. Instead of burning compute 🔥💸 on trivial problems, it allocates 5x more on problems that actually need it ↓
Our new method (ALP) monitors solve rates across RL rollouts and applies inverse difficulty penalties during RL training. Result? Models learn an implicit difficulty estimator—allocating 5x more tokens to hard vs easy problems, cutting overall usage by 50% 🧵👇1/10
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Our new method (ALP) monitors solve rates across RL rollouts and applies inverse difficulty penalties during RL training. Result? Models learn an implicit difficulty estimator—allocating 5x more tokens to hard vs easy problems, cutting overall usage by 50% 🧵👇1/10
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9/10 Shoutout to the amazing contributors from @synth_labs & @Stanford: @ZiyuX @ChaseBlagden @rm_rafailov @NathanThinks @sangttruong @chelseabfinn @nickhaber And to @tractoai @nebiusai for providing R&D compute!
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10/10 Bottom line: ALP teaches models to think harder on hard problems and think less on easy ones - exactly what we want for efficient reasoning! 📄 Paper: arxiv.org/abs/2506.05256 🔧 Models: synthlabs.ai/research/adapti…
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Generative Reward Models impact compounds daily. way stronger interest now than when we published last fall 👇 many excellent recent extensions—cool seeing where researchers take GenRM
we bootstrapped our way to generalized meta-reasoning capabilities with generative reward models classical reward models can be worse than random on new reasoning tasks 🎲 we see improvements in robustness, generalization, interpretability and an opportunity to unify RLHF/RLAIF ...it's rationales all the way down 🐢
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btw we have ongoing research on this front! we're open-science, pro-publication, and love collaboration. want to push this frontier forward? we're growing our SF team & always open to research partners—reach out, my DMs are open 📩
excellent work by @jaseweston & team—extending our "Generative Reward Models" work with RL (GRPO) to optimize LLM reasoning during judgment scalable (synthetic) evaluation continues to be AI's key bottleneck!
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SynthLabs retweeted
Read how @synth_labs, a startup developing AI solutions tailored for logical reasoning, is advancing AI post-training with our @TractoAI: nebius.com/customer-stories/… 🔹 Goal: Develop an ML system that empowers reasoning models to surpass pattern matching and implement sophisticated search and exploration strategies. 🔹 Solution: Build scalable training infrastructure. Reasoning models require large datasets and distributed computing, making multi-node training and high-performance GPUs essential for effective results. 🔹 Results: Using TractoAI, a serverless platform on Nebius AI Cloud, SynthLabs trains AI reasoning models, laying the foundation for next-gen reasoning systems and enterprise use.
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btw, random fun fact we pointed out months ago: the only MATH example @OpenAI published with o1 announcement included an unsubstantiated assumption 😬
> still hacks at a fairly high rate > we wouldn't notice this agent was misaligned Meanwhile, industry: aggressively distilling Meta-CoT slop directly into models 🫡
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SynthLabs retweeted
The final stop in our meetup series will be in San Francisco! 🌁 nebius.com/events/nebius-roa… Join us at Convene 100 Stockton near Union Square on Thursday, March 13, for a deep dive into our AI cloud. Our developers, AI R&D engineers and architects will share insights with the tech community on how we build AI Cloud Accelerated by NVIDIA and contribute to the AI field and open source. 🛍️ What’s in store? - An insider’s look at Nebius architecture and development principles - A deep dive into how we developed Soperator, our K8s operator for Slurm - Discover how test-time computation unlocks the potential of agentic systems - We’ll also be joined by the co-founders of @synth_labs, telling how they use our serverless platform TractoAI Plus, attendees will receive promo codes to test our cloud and inference-as-a-service. ⚡ Spaces are limited! Learn more and register now on the Nebius website to secure your spot: nebius.com/events/nebius-roa…
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Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models Author's Explanation: xitter.cf/synth_labs/status/1894… Overview: Big-Math, a dataset of over 250,000 high-quality math questions with verifiable answers, is purposefully designed for reinforcement learning (RL) in LLMs, addressing the limitations of existing datasets that compromise on either quality or quantity. The dataset is created through a rigorous filtering, cleaning, and curation of openly available datasets, supplemented by 47,000 new questions generated via a systematic reformulation algorithm that converts closed-ended questions to open-ended ones, significantly larger than commonly used datasets like GSM8k and MATH. Big-Math exhibits a high degree of diversity across problem domains and incorporates a wide range of difficulty levels, making it suitable for various model capabilities and training. Paper: arxiv.org/abs/2502.17387
Releasing Big-MATH—the first heavily curated & verifiable dataset designed specifically for large-scale RL training & LLM reasoning! 📝 250,000+ problems, 47k NEW Q's ✅ 10x larger than existing datasets like MATH 🧑‍⚖️ Verifiable—we eliminated 400k+ problems Details below! 🧵👇
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Big-Math: Big-Math: Massive Math Dataset for RL Training - 10x larger than GSM8k/MATH - 3 core properties: uniquely verifiable, open-ended, closed-form - Human-validated 90%+ precision filters - Difficulty metrics for curriculum learning
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