arXiv AI
Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents
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arXiv AI
1d ago
Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution
The paper introduces neuro‑symbolic computer use, a method that learns reusable policies to execute recurring computer workflows efficiently. Instead of re‑planning each run, the learned policy encodes stable decisions (ordering, variables, loops, branches) into executable code while delegating observation‑dependent decisions to neural models. Using neuro‑symbolic policy iteration, the approach iteratively refines the policy from a single agent trajectory, diagnoses failures, and revises the code with a coding model, achieving superior Pass^3 scores and significant reductions in per‑run cost and latency on OSWorld‑Verified and ScienceBoard benchmarks.
arXiv AI
Jun 2
On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
arXiv:2603. 12109v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons.
arXiv AI
Jul 3
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
arXiv:2607. 01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal.
arXiv AI
Sep 2
Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents
The paper proposes CANOPY, a minimalist reinforcement learning protocol that addresses two common pitfalls—signal starvation and policy drift—in outcome‑only RL for long‑horizon interactive tasks. By scaling same‑task exploration, keeping updates on‑policy, and anchoring updates with KL divergence, CANOPY enables a Qwen3‑14B agent to achieve top leaderboard results on the AppWorld coding benchmark without auxiliary supervision or elaborate scaffolding. The approach also improves performance on SWE‑bench for a Qwen3.5‑9B model.
arXiv AI
Sep 23
ORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI
The paper introduces ORDER, a fictitious-world benchmark designed to evaluate domain-adaptive embodied AI. ORDER consists of a synthetic 342,069-token corpus defining a self-consistent physics, a 500-question knowledge test (ORDER‑BENCH), and a compositional spatial task (ORDER‑SPATIAL) that requires ordering objects for safe manipulation. The benchmark demonstrates that models like GPT‑4.1 perform poorly without adaptation, while small models improve significantly after continual pre‑training, and that performance on ORDER‑SPATIAL better predicts real plan quality than knowledge-test accuracy.
arXiv AI
Jul 20
Understanding Reasoning from Pretraining to Post-Training
arXiv:2607. 16097v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it.