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ARXIV:2605.20477 · AGENTS · SUBMITTED 21 MAY · 20:30 UTC · FRESHNESS STALE
ARXIV:2605.20477AGENTSSUBMITTED 21 MAY · 20:30 UTCFRESHNESS STALEYuval Shalev · Zifeng Ding · Mateja Jamnik · arXiv
A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization.
Opportunity summary
Pain A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization. Whether such experience can be distilled into reusable lessons that improve performance on future unseen…
Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear. Code availability is flagged in the…
Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization.
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10.48550/arXiv.2605.20477A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization.
Abstract
Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear. We address this problem by introducing the In-context Training (ICT) task, a framework for evaluating cross-task self-improvement in language agents. In ICT, a reflector model observes trajectories collected by an actor model and generates system prompts intended to improve the actor's performance on future unseen tasks. We then propose an RL-based training pipeline for learning such reflections directly from experience, without human-provided examples. Across ALFWorld and MiniHack, our trained reflectors outperform an untrained baseline on most held-out task families, showing that the ability to learn from experience can itself be learned. In some cases, we observe generalisation beyond the benchmark on which the reflector was trained, to substantially different environments. Finally, we introduce MetaGym, a generic Python library for constructing meta-environments, enabling future research on self-improving language agents.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclea...
METHOD
Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear. Code availability is flagged in the production record; the public repository...
WHY NOW
Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework and training pipeline for language agents to learn reusable lessons from experience, enabling cross-task self-improvement and generalization.
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Commercial read
7.0/10 public viability
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Build Passport
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status
missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Buyer clarity
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missing
Current read
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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