Tag: Reinforcement Learning

All the articles with the tag "Reinforcement Learning".

• Can RFT Learn From Failure? Negative Signals for Agent Fine-Tuning

  20 Sep, 2026 · 16 min read

  A technical guide to learning from failed agent rollouts through step masking, unlikelihood, DPO, SimPO, and step-level credit assignment.

• From GRPO Outcome Rewards to Token-Level Advantage

  Updated: 6 Sep, 2026 · 22 min read

  A practical framework for turning GRPO-style sequence rewards into token-level advantages, including GAE-style estimators, credit assignment routes, and multi-reward training design.

• What Makes an Agent Swarm Work? Simple Rules, Information Boundaries, and the Value of Verification

  5 Sep, 2026 · 37 min read

  A theory-and-experiment study of agent swarm organization: operational roles, marginal-contribution incentives, selective information sharing, and why verification remains the limiting assumption.

• Do Verifier Errors Grow Superlinearly with Horizon? A Six-Stage Experiment

  Updated: 17 Aug, 2026 · 17 min read

  A preregistered 224-artifact study confirms a 66% hybrid-verifier improvement, does not support the universal superlinear headline, and finds that task structure can reverse the apparent horizon effect.

• From Tool Calls to Policy Updates: A Reproducible Agent RL Stack

  14 Aug, 2026 · 45 min read

  A reproducible blueprint for Agent RL: environment snapshots, verifier contracts, credit assignment, three-policy semantics, asynchronous generation and training, partial rollouts, and runnable smoke tests.

• A Repair Can Save a Trajectory Without Teaching the Agent

  14 Aug, 2026 · 18 min read

  An eight-seed counterfactual-replay experiment found that high-value one-action repairs reliably rescue failed agent trajectories—but a repair-value-dominant SFT selector did not beat simpler rules and recovered only 25% of matched-RL improvement.