HuggingFace Papers
8/10 signal
Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
agenticeval
What happened
The authors present Long-Horizon-Terminal-Bench, an evaluation suite featuring 46 complex terminal-based tasks across nine categories (e.g., software engineering, scientific computing). Unlike traditional benchmarks that only grade the final output, this benchmark decomposes tasks into fine-grained, graded subtasks to provide dense intermediate rewards and partial credit. Tasks typically require hundreds of steps and hours of execution.
Why it matters
It solves the sparse reward problem in agent evaluation by grading intermediate progress on long-running terminal tasks.
The take
Evaluating agents on long-horizon tasks has been notoriously difficult due to sparse feedback; an agent either succeeds or fails after 100 steps. Introducing dense, graded checkpoints is exactly how we should evaluate and train RL-based agents. This benchmark provides a much-needed, realistic testing ground for complex, multi-step agentic workflows.
Do this
Review the Long-Horizon-Terminal-Bench GitHub repository and consider adapting its dense-reward grading methodology for your own internal agent evaluations.
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