RLPE: Teaching LLMs to Prompt with Reinforcement Learning
To close this gap, we introduce Reinforcement Learned Prompt Engineering (RLPE), which trains a model to analyze traces of a separate task model and write a prompt targeting the failures it observes.
ProofPaper ↗
Key points
- Prompting is a widely used method for adapting general-purpose LLMs to domain-specific tasks.
- Effective prompts can be tedious to create manually, so prompt writing is increasingly handed to LLMs, with recent work training LLMs to write prompts for other models.
- These techniques either write prompts without observing how the task model behaves, blind to the failure modes the prompt should address, or must run alongside the task LLM at inference time, adding overhead to every query.
- We train a 9B-parameter model that writes better prompts than state-of-the-art frontier models (gpt-5.6-sol and claude-opus-5) and prompt optimizers (GEPA and TextGrad) on 2/3 benchmarks.
Sources (1)
- [1]RLPE: Teaching LLMs to Prompt with Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:59 PM
To close this gap, we introduce Reinforcement Learned Prompt Engineering (RLPE), which trains a model to analyze traces of a separate task model and write a prompt targeting the failures it observes.
Prompting is a widely used method for adapting general-purpose LLMs to domain-specific tasks.
Extractive summary: sentences quoted from the sources.
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