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Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language often provides a much richer learning medium for LLMs, compared to policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error. Given any AI system containing one or more LLM prompts, GEPA samples trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain. Across six tasks, GEPA outperforms GRPO by 6% on average and by up to 20%, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10% (e.g., +12% accuracy on AIME-2025), and demonstrates promising results as an inference-time search strategy for code optimization. We release our code at https://github.com/gepa-ai/gepa .
AI built for autonomy crowds people out, making us passive observers of what’s coming. We’re building toward a different future.
Using Jacobian Lenses to peer into what an LLM is thinking when it sees you.
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Left: Telegram digest (optional); Right: detailed digest on HTML Like probably everyone here, my to-read list only grows. Skimming arXiv listings or my feeds takes 30-60 minutes a day, 95% of it is irrelevant to what I actually work on, and newsletters don't really help: they surface what's popular, not what's relevant to my research. So I built Research Radar, a daily cron job that: Fetches every new paper in your arXiv categories (RSS + API, deduped) Scores every abstract 1-10 against a markdown file describing your research interests (cheap model, batched) Deep-reads the top scorers: downloads the PDF, extracts full text, and a strong model writes a summary, key insights, limitations, and how it relates to your own work Delivers a morning HTML digest + optional Telegram ping with the must-reads Design decisions: Nothing domain-specific in the code. Your interests live in one markdown file. Edit it and the same pipeline works for ML, physics, bio, econ, whatever Only the two scoring passes touch a model. Fetching, dedup, PDF extraction, and rendering are deterministic Python Model-agnostic backend layer: Claude Code / Codex CLIs run it on the subscription you already pay for (no API key), or any OpenAI-compatible endpoint, including fully local via Ollama / vLLM. Backends mix per pass: cheap model for skimming, strong one for the 5-10 deep reads Approximate costs, benchmarked in the repo (tokens, cost, latency, quality grades per model). Rough sizing: a 10-abstract scoring batch is ~18k input tokens with a small JSON out; a deep read sends the whole paper, 40-70k input tokens. I've been using it daily for about a month and it's been genuinely useful in my field. The repo is a generalization of my personal setup to any domain, and I haven't deeply tested fields other than mine, so feedback and GitHub issues are very welcome. The model has to say "not relevant, 3/10" a lot without drifting toward score inflation, and the whole scoring is just prompt + markdown context. So I'm curious how others would approach calibrating an LLM judge like this GitHub: https://github.com/ramazan793/research-radar submitted by /u/usedtobreath [link] [Kommentare]
