The Tasteful Agent: Measuring and Improving Taste in Long-Horizon Tasks
Researchers introduce Taste-Bench, a benchmark measuring LLM agents' ability to make good long-horizon decisions at critical decision forks.
This paper introduces Taste-Bench, a benchmark designed to measure the 'taste' of LLM agents, defined as their ability to make good long-horizon decisions at critical forks. The benchmark is automatically constructed from agent trajectories in engineering and research tasks, presenting decision points where one direction leads to a better outcome. Evaluation shows that even the best frontier model answers only 59.7% of taste questions correctly. The researchers also demonstrate that taste can be improved by distilling judgment from a teacher model that has access to the full trajectory outcome.
- Introduces Taste-Bench to measure 'taste' in LLM agents for long-horizon tasks.
- Taste is defined as the ability to make good long-horizon decisions at decision forks.
- The best model achieves only 59.7% accuracy on the benchmark.
- Taste can be improved by distilling judgment from a teacher model that sees outcomes.
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From the original article
1 min read at huggingface.co
LLM agents increasingly work on long-horizon tasks, and the decisions they make along the way, such as which hypothesis to test or which implementation to build on, determine the outcome of the whole run. Making these decisions well is becoming a key capability for both engineering and research agents. We refer to the ability to make good long-horizon decisions as the taste of an agent.
By Wenbo Pan, Zhichao Liu, Shujie Liu · huggingface.co
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