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# Parameter Golf with AutoResearch — Vayum Arora, Zhengyao Jiang, Dixing Xu & Dhruv Srikanth, Weco AI

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 34 min read · 0 upvotes · 0 comments

## Summary

An agent keeps the code changes that improve a language model's score and discards the rest. Weco AI turns that research loop into a workshop, using Mini Parameter Golf to make training experiments small enough to inspect as they run. Zhengyao Jiang introduces autoresearch as repeated proposals and evaluations, and Dixing Xu explains the team's Aiden system and its contributions to OpenAI's Parameter Golf challenge. Vayum Arora and Dhruv Srikanth then connect those results to a practical setup that combines a modifiable source file, a fixed evaluation command and a measurable goal.

The walkthrough focuses on what determines whether the search is useful: fast and stable evaluations, a clear direction for the metric, time limits, initial guidance and protection against reward hacking. Weco's experiment tree makes each attempt inspectable, with its code changes and score visible in the dashboard. The presenters show how to branch from promising work and steer a running search toward a different idea instead of simply letting it continue. Internet problems lead to a guided setup demonstration, followed by questions about reinforcement learning and combining multiple objectives. The final exercise swaps model training for a Flappy Bird controller scored across normal play, moving gaps, wind and mixed conditions. A hidden validation set and limits on what the controller can edit make the competition another lesson in designing an evaluation that rewards the behavior you want.

Speaker info:
Vayum Arora:
- https://www.linkedin.com/in/vayum-arora/
Zhengyao Jiang:
- https://zhengyaojiang.github.io/
- https://www.linkedin.com/in/zhengyao-jiang-387b44145/
Dixing Xu:
- https://dex.moe/
- https://linkedin.com/in/dex-xu
Dhruv Srikanth:
- https://www.linkedin.com/in/dhruv-srikanth
Workshop resource:
- https://github.com/WecoAI/mini-pg

Timestamps:
0:00 - Presenters and the autoresearch loop
3:35 - Workshop plan and AIDE background
5:39 - Parameter Golf and Aiden
8:30 - Mini Parameter Golf
9:37 - Weco, Modal, and repository setup
11:10 - Anatomy of an autoresearch run
12:06 - Fast evaluations and reward hacking
13:02 - Define the metric and search budget
14:25 - Time limits and initial guidance
16:31 - Structure, observability, and steering
17:29 - Inspect the experiment tree
19:36 - Human ideas and research breakthroughs
20:47 - Guided setup and live dashboard
24:41 - Reinforcement learning and multiple objectives
26:18 - Evaluate and steer an experiment
28:46 - The Flappy Weco competition
30:08 - Environments, validation, and rules
32:30 - Exercise instructions and wrap up

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=7RFJRDYroic>

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Tags: [#ai](https://daily.dev/tags/ai), [#ai-engineering](https://daily.dev/tags/ai-engineering)

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