In economics, deflation is the opposite of inflation—it’s what we call it when prices go down instead of up. It is generally considered harmful: both because it is usually brought on by something really bad (like a severe economic contraction), and because in and of itself, it has knock-on effects on consumer behavior that can lead to a death spiral. One of the main problems is that if people expect prices to keep going down, they’ll delay purchases and save more, because they expect that they’ll be able to get the stuff for less later. Less spending means less demand means less revenue means fewer jobs which means less spending and then whoops you’re in a deflationary spiral.
… This isn’t really an economics blog post, though. I’m thinking about deflation because it parallels a recent pattern I’m seeing in startups. (So I guess you could call it a micro-economics blog post?) The basic mechanism is: (1) it’s easier and cheaper to build software now than ever before; (2) it seems like it probably will keep getting easier and cheaper for the forseeable future; so (3) why bother building anything now, just build it later when it’s cheaper and easier. — Read More
Daily Archives: November 28, 2025
DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning
Large language models have made significant progress in mathematical reasoning, which serves as an important testbed for AI and could impact scientific research if further advanced. By scaling reasoning with reinforcement learning that rewards correct final answers, LLMs have improved from poor performance to saturating quantitative reasoning competitions like AIME and HMMT in one year. However, this approach faces fundamental limitations.
Pursuing higher final answer accuracy doesn’t address a key issue: correct answers don’t guarantee correct reasoning. Moreover, many mathematical tasks like theorem proving require rigorous step-by-step derivation rather than numerical answers, making final answer rewards inapplicable.
To push the limits of deep reasoning, we believe it is necessary to verify the comprehensiveness and rigor of mathematical reasoning. Self-verification is particularly important for scaling test-time compute, especially for open problems without known solutions. Towards self-verifiable mathematical reasoning, we investigate how to train an accurate and faithful LLM-based verifier for theorem proving. We then train a proof generator using the verifier as the reward model, and incentivize the generator to identify and resolve as many issues as possible in their own proofs before finalizing them. — Read More
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