LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks

• URL: http://arxiv.org/abs/2607.18110v1
• Date: Mon, 20 Jul 2026 16:08:49 GMT
• Title: LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
• Abstract summary: We propose Experiential Learning (EL), which repurposes the feedback model from an LLM-as-a-Judge into an LLM-as-a-Coach.<n>EL consistently outperforms rubric-based RL on held-out and unseen open-ended tasks.<n>These findings establish experiential knowledge as a richer and more generalizable learning signal for post-training on non-verifiable tasks.
• Score: 89.13372484640392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) on open-ended tasks compresses an LLM's rubric-based evaluation into a scalar reward, discarding rich textual feedback and conflating responses with distinct quality profiles. We propose Experiential Learning (EL), which repurposes the feedback model from an LLM-as-a-Judge into an LLM-as-a-Coach. The coach distills its assessment of each on-policy response into transferable experiential knowledge, which conditions a teacher model and is internalized by the policy through on-policy context distillation. Compared with scalar rewards, this higher-bandwidth feedback channel provides dense supervision and preserves fine-grained preferences among high-quality responses. Across two policy families, with feedback from the policy itself or a proprietary model, EL consistently outperforms rubric-based RL on held-out and unseen open-ended tasks. Notably, EL generalizes better beyond the training distribution, and mitigates reward hacking. These findings establish experiential knowledge as a richer and more generalizable learning signal for post-training on non-verifiable tasks.

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