{"slug": "harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl", "title": "HarnessRL: Co-Evolving Policies with a Just-in-Time Training Harness for Open-Ended RL", "summary": "A research paper titled \"HarnessRL: Co-Evolving Policies with a Just-in-Time Training Harness for Open-Ended RL\" identifies two failure modes in rubric-based reinforcement learning for open-ended generation, where natural-language criteria serve as rewards: under non-discriminative rewards, scores collapse to uniformly low or high values as training progresses. The work proposes co-evolving policies with a just-in-time training harness to address the degradation of rubric-based rewards.", "body_md": "Rubric-based reinforcement learning (RL) trains policies for open-ended generation with natural-language criteria as rewards, but these rewards can become ineffective as training progresses. We identify two failure modes. Under non-discriminative rewards, scores collapse to uniformly low or high val", "url": "https://wpnews.pro/news/harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl", "canonical_source": "https://aiflash.com/news/136518/", "published_at": "2026-10-21 05:30:02+00:00", "updated_at": "2026-10-21 05:47:55.916277+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "large-language-models", "artificial-intelligence"], "entities": ["HarnessRL"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl", "markdown": "https://wpnews.pro/news/harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl.md", "text": "https://wpnews.pro/news/harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl.txt", "jsonld": "https://wpnews.pro/news/harnessrl-co-evolving-policies-with-a-just-in-time-training-harness-for-open-rl.jsonld"}}