Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy
Post-training Large Language Models (LLMs) with Reinforcement Learning (RL) has become an important tool for improving model capabilities, but the LLM action-space structure introduces challenges distinct from classical RL, with implications for inducing exploration. New methods are required that leverage the broad knowledge and flexibility of pre-trained LLMs to deliberately generate diverse experience at training time. We propose Instruction-Conditioned Exploration (ICE), which appends one of a small fixed set of instructions to task prompts during training, using the same set for every problem, increasing the coverage of behaviours attempted. To facilitate ICE, we combine RL on the ins...
| Authors | Jim Dilkes, Vahid Yazdanpanah, Sebastian Stein |
|---|---|
| Primary category | cs.AI |
| Published | 3 August 2026 |
| Last updated at source | 5 August 2026 |
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