WHALE: a simple recipe to jointly optimize an LLM's weights and harness
lateinteraction · x · 2026-09-04
Researcher yoonholeee introduces WHALE, a simple recipe for jointly optimizing an LLM's weights and its harness, with blog and paper released.
- Key observation: harness optimization is sample-efficient but plateaus.
- If you can afford to update the model too, jointly optimizing weights and harness breaks through that ceiling.
Related event: WHALE: Jointly Optimizing LLM Weights and Agent Harness(2 posts)→
More from coding & agent
- Google researcher warns shift from C++ to Python creates devs who don't understand computers — chaumian · 2026-09-04
- FastAPI Conf 2026 lineup: Pydantic author to talk AI-assisted coding — samuelcolvin · 2026-09-04
- Pragmatic Engineer launches essay contest on how AI is changing software engineering, $10K grand prize — neal_lathia · 2026-09-04
- Cursor subscriptions quietly include a managed multi-agent bot service, possibly the cheapest way to start — algo_diver · 2026-09-04
- This MCP server gets smarter as your team uses it, via session_id task memory — h4xz13 · 2026-09-04
- When stuck, the model runs 1000 commands; I go get coffee and think — moultano · 2026-09-04