Why we built a benchmark before training for instruction following

Researchers found that reinforcement learning on conventional instruction-following rewards can produce superficially high scores while encouraging models to omit the requested task, as demonstrated by a model that answered a detailed alternate-history prompt with only “China.” They created LIFT-Bench, a synthetic drafting benchmark that attaches formatting and content requirements to specific document sections, using code for verifiable constraints and a scoped LLM judge for content requirements. Testing showed that judges perform substantially better when given only the relevant section rather than an entire document, and that model rankings differ between structured, section-scoped tasks and conventional flat prompts. Using LIFT-Bench’s scoring system as the reward in LIFT-Train prevented the severe response shortening observed with IFTrain training and maintained performance on external content-sensitive benchmarks, although improvements were characterized primarily as avoiding regression rather than delivering broad gains. The authors conclude that instruction-following training should evaluate task completion alongside formal constraints, validate reward judges against human labels, select models using relevant task formats, and monitor output length as well as reward during training.

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