Rishabh Tiwari

Can LLMs Self-Verify? Much better than you'd expect. LLMs are increasingly used as parallel reasoners, sampling many solutions at once. Choosing the right answer is the real bottleneck. We show that pairwise self-verification is a powerful primitive. Introducing V1, a framework that unifies generation and self-verification: 💡 Pairwise self-verification beats pointwise scoring, improving test-time scaling 💡 V1-Infer: Efficient tournament-style ranking that improves self-verification 💡 V1-PairRL: RL training where generation and verification co-evolve for developing better self-verifiers 🧵👇

Introducing SPECS (SPECulative test time Scaling), a test-time scaling (TTS) algorithm with pareto-frontier latency/accuracy trade-off. Scaling test-time compute improves LLM reasoning but imposes a latency overhead. Prior work optimizes TTS accuracy as a function of FLOPS, we propose to further reduce latency by addressing the memory bottleneck of LLM inference through speculative drafts. See a breakdown of the method below. (1/n) 🧵 👇