[Paper] Iterative Deployment Improves Planning Skills in LLMs

Source: arXiv - 2512.24940v1

Overview

The paper demonstrates that repeatedly deploying a large language model (LLM), then fine‑tuning the next generation on the user‑curated outputs of the previous one, can dramatically boost the model’s planning abilities. By treating the deployment loop as an implicit reinforcement‑learning (RL) process, the authors show that later models not only solve harder planning problems but also start to generate much longer, more generalizable plans than the original model.

Key Contributions

• Iterative Deployment Framework – Proposes a simple, repeatable pipeline: deploy → collect user‑selected successful plans → fine‑tune the next model on this curated data.
• Empirical Boost in Planning Skills – Across several benchmark planning domains, later‑generation models achieve higher success rates and discover plans up to an order of magnitude longer than the seed model.
• Theoretical Link to RL – Shows that the outer‑loop of iterative deployment is mathematically equivalent to RL with an implicit reward function derived from user curation.
• Safety Insight – Highlights that the emergent reward is not explicitly defined, raising potential AI‑safety concerns about unintended behavior as the loop progresses.
• Alternative to Explicit RL – Positions data‑curation‑driven fine‑tuning as a viable training regime when designing reward functions is difficult or risky.

Methodology

1. Seed Model – Start with a pretrained LLM (e.g., GPT‑3‑style) that can generate candidate plans for a given task.
2. Deployment & Data Collection – Release the model to users (or simulated agents) who evaluate the generated plans. Users keep only the successful plans (those that achieve the goal).
3. Curated Dataset Construction – The retained plans, together with their prompts, form a high‑quality training set reflecting what “works” in the environment.
4. Fine‑Tuning – The next‑generation LLM is fine‑tuned on this curated set, inheriting the demonstrated planning patterns.
5. Repeat – Steps 2‑4 are iterated several times, each cycle producing a model that has seen more refined examples of successful planning.

The authors evaluate the pipeline on classic planning benchmarks (e.g., block‑stacking, navigation grids, symbolic logistics) and compare against a baseline that receives only the original pretraining data.

Results & Findings

• Longer Plans: Later models consistently produce plans that are 2–4× longer, indicating they have learned to decompose complex goals into finer sub‑steps.
• Emergent Generalization: Even on problem instances never seen during curation, the models extrapolate the planning strategy, solving tasks that would require substantially deeper reasoning.
• RL Analogy: The theoretical analysis proves that each iteration maximizes an implicit reward equal to “plan success as judged by the user,” mirroring policy‑gradient RL without an explicit reward signal.

Practical Implications

• Rapid Skill Bootstrapping – Teams can improve domain‑specific reasoning (e.g., workflow automation, code synthesis, robotics) by simply collecting successful outputs from deployed models rather than engineering complex reward functions.
• Cost‑Effective Fine‑Tuning – The curated dataset is typically orders of magnitude smaller than full reinforcement‑learning roll‑outs, reducing compute and annotation costs.
• Safety Monitoring – Since the reward emerges from user selection, developers must audit the curation process to avoid reinforcing undesirable shortcuts or hidden biases.
• Product Development Loop – The framework fits naturally into a continuous‑delivery pipeline: release → monitor → harvest successes → retrain → redeploy, enabling a data‑driven improvement cycle for AI‑assisted tools.

Limitations & Future Work

• Dependence on High‑Quality Curation – The approach assumes users can reliably identify successful plans; noisy or adversarial feedback could degrade performance.
• Scalability to Very Large Tasks – While plan length grew, the method was tested on relatively bounded benchmark domains; scaling to open‑world planning (e.g., full‑stack software deployment) remains open.
• Safety Guarantees – The implicit reward is opaque, making it hard to predict unintended emergent behaviors; formal safety analyses are needed.
• Future Directions – The authors suggest exploring automated curation (e.g., using simulators), combining explicit RL signals with the iterative loop, and applying the technique to other reasoning modalities such as theorem proving or multi‑agent coordination.

Authors

• Augusto B. Corrêa
• Yoav Gelberg
• Luckeciano C. Melo
• Ilia Shumailov
• André G. Pereira
• Yarin Gal

Paper Information

• arXiv ID: 2512.24940v1
• Categories: cs.AI, cs.CL, cs.LG
• Published: December 31, 2025
• PDF: Download PDF