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Learning to solve hard problems in RL for LLMs by never giving up
The post introduces the *Matthew Effect* in RL‑fine‑tuning of LLMs—performance gains concentrate on tasks the model already solves— and proposes *Never Give Up* (NGU), an adaptive sampling scheme that uses a small k for easy prompts and retries hard prompts with a high‑probability “never give up” loop. Experiments on math (AIME, GSM8k), code (Manufactoria), and larger‑scale setups (DeepScaler) sh…
Hacker News front pagegithub.io11 minHN1179Article: Your Next DSL Author Is a Language Model
Typed Domain Grounding (TDG) embeds a DSL inside a mainstream language the LLM already knows (e.g., Kotlin) and uses the host compiler as an oracle. The author describes five building blocks—embedding, choosing a host language with high training‑data frequency, compiler‑driven type safety, a generate‑compile‑repair loop, and an on‑demand teaching tool—and shows measured results from kUML, a Kotli…
InfoQinfoq.com18 minLies, Damn Lies and Benchmarks
Codename One engineers dissect why benchmark numbers can be misleading, then share concrete work on GC tuning, proper weak/soft references, and a new probing sequence for their open‑addressed HashMap that cuts miss‑probe counts from >16 k to ~1.5 per lookup.
CodeName Onecodenameone.com20 minWangNet – 1.8 MB, zero-dependency Numberwang adjudication in 11 languages
Numberwang is a tiny character‑level CNN packaged as a 1.8 MB JSON model and a ~100‑line pure‑Python inference script that classifies numeric strings in eleven languages without any external dependencies. It reaches 88.9 % macro‑F1 on a held‑out set, but struggles with unseen arithmetic expressions.
Hacker News front pagegithub.com2 minreleaseHN18062Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models
The paper reframes fine‑tuning of instruction‑tuned LLMs as a direction‑selection problem under a fixed behavioral‑drift budget, showing that the update direction, not magnitude, determines trade‑offs between target performance and capability preservation. In QA‑only fine‑tuning of Qwen‑3 models, layer‑selective probing finds effective directions that boost scientific reasoning and multilingual t…
Hugging Face Daily Papersarxiv.org1 minpaper
