VPD frames language feedback learning as variational EM so the teacher policy refines itself via trust-region updates on outcomes while the student learns dense token distributions on its own rollouts, outperforming fixed-teacher baselines on reasoning and code tasks.
Feedback descent: Open-ended text optimization via pairwise comparison
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Meta-Harness discovers improved harness code for LLMs via agentic search over prior execution traces, yielding 7.7-point gains on text classification with 4x fewer tokens and 4.7-point gains on math reasoning across held-out models.
Life-Harness evolves reusable runtime interventions from training failures to improve frozen LLM agents by 88.5% on average across 126 settings in seven deterministic environments while transferring across 18 model backbones.
CASCADE enables LLMs to continually adapt at deployment via case-based episodic memory and contextual bandits, improving macro-averaged success by 20.9% over zero-shot on 16 tasks spanning medicine, law, code, and robotics.
EvoSkill evolves agent skills via failure analysis and Pareto frontier selection, raising exact-match accuracy 7.3% on OfficeQA and 12.1% on SealQA with 5.3% zero-shot transfer to BrowseComp.
citing papers explorer
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Learning from Language Feedback via Variational Policy Distillation
VPD frames language feedback learning as variational EM so the teacher policy refines itself via trust-region updates on outcomes while the student learns dense token distributions on its own rollouts, outperforming fixed-teacher baselines on reasoning and code tasks.
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Meta-Harness: End-to-End Optimization of Model Harnesses
Meta-Harness discovers improved harness code for LLMs via agentic search over prior execution traces, yielding 7.7-point gains on text classification with 4x fewer tokens and 4.7-point gains on math reasoning across held-out models.
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Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
Life-Harness evolves reusable runtime interventions from training failures to improve frozen LLM agents by 88.5% on average across 126 settings in seven deterministic environments while transferring across 18 model backbones.
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CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment
CASCADE enables LLMs to continually adapt at deployment via case-based episodic memory and contextual bandits, improving macro-averaged success by 20.9% over zero-shot on 16 tasks spanning medicine, law, code, and robotics.
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EvoSkill: Automated Skill Discovery for Multi-Agent Systems
EvoSkill evolves agent skills via failure analysis and Pareto frontier selection, raising exact-match accuracy 7.3% on OfficeQA and 12.1% on SealQA with 5.3% zero-shot transfer to BrowseComp.