arXiv Machine Learning
Reinforcement Learning for Symbolic Equation Solving
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arXiv Machine Learning
Jul 16
TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
arXiv:2607. 13988v1 Announce Type: new Abstract: Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training.
arXiv Machine Learning
Jul 17
Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
arXiv:2607. 14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes.
arXiv AI
Aug 18
Adaptive Mixing of Policies from Searching and Policies from Learning
arXiv:2608. 15700v1 Announce Type: new Abstract: Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long.
arXiv Machine Learning
Aug 10
Learning Suffers More Than the Policy Class Under Partial Observability: A Closed-Form Analysis
arXiv:2608. 07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one.
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 Machine Learning
Aug 4
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
arXiv:2608. 00335v1 Announce Type: cross Abstract: Compact web agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL).