Separate Evals from Code in Autoresearch

🤖Read original on Reddit r/MachineLearning

#evaluation #agent-optimization #experiment-tracking #tddautoresearch-eval-separation-skillclaude-code autoresearch karpathy

💡Learn how fixed, version-controlled evals can make long-running AI optimization loops safer and more auditable.

⚡ 30-Second TL;DR

What Changed

The most important human work is designing robust evaluation metrics, optimization objectives, and constraints.

Why It Matters

This workflow could make autonomous experimentation more trustworthy by preventing agents from silently improving scores through evaluation changes. Its effectiveness still depends on whether the initial evals adequately represent real-world goals and resist gaming.

What To Do Next

Create a version-controlled, read-only eval directory for your next Claude Code optimization loop and require every experiment to append results to an HTML journal.

Who should care:Researchers & Academics

Key Points

•The most important human work is designing robust evaluation metrics, optimization objectives, and constraints.
•Fixed, version-controlled evals—called “hills”—make attempts to manipulate evaluation logic visible and auditable.
•The agent records experiments and findings in an HTML journal while Claude Code runs optimization loops for days.

🧠 Deep Insight

AI-generated analysis for this event.

🔑 Enhanced Key Takeaways

•The 'Separate Evals from Code' methodology aligns with the emerging 'Evaluation-Driven Development' (EDD) paradigm, which treats evaluation suites as immutable test harnesses to prevent Goodhart's Law in autonomous agent training.
•This workflow leverages Claude Code's ability to maintain persistent state across long-running sessions, effectively utilizing the agent's context window as a scratchpad for iterative hypothesis testing.
•The use of HTML journals for experiment tracking facilitates 'human-in-the-loop' auditing, allowing researchers to parse complex agent reasoning chains without needing to inspect raw CLI logs.
•Version-controlled 'hills' (evaluations) are increasingly being adopted in agentic research to mitigate 'reward hacking,' where agents optimize for the metric rather than the intended task objective.
•This approach addresses the 'brittleness' of autonomous research loops by decoupling the agent's creative code generation from the rigid, deterministic validation logic required for scientific reproducibility.

🛠️ Technical Deep Dive

Implementation utilizes a strict separation of concerns where the evaluation harness is defined as a read-only dependency for the agent's workspace.
Claude Code is configured with a persistent session state, allowing the agent to maintain a local SQLite or JSON-based experiment database across multi-day execution cycles.
The HTML journal acts as a structured output format, enabling the agent to render visual plots and tables directly within the workspace for easier human review.
Version control for 'hills' is typically managed via Git submodules or dedicated repository branches, ensuring that any modification to the evaluation logic requires a separate, human-approved commit.

🔮 Future ImplicationsAI analysis grounded in cited sources

Autonomous research agents will shift toward 'Evaluation-as-Code' (EaC) standards.

Standardizing evaluation harnesses will become necessary to ensure reproducibility and prevent reward hacking in long-running autonomous research loops.

Human oversight will transition from code review to 'metric design' review.

As agents become more capable of self-correcting code, the primary bottleneck for research quality will shift to the robustness of the evaluation objectives themselves.

🤖Read original article on Reddit r/MachineLearning

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