Tag: Reinforcement Learning

All the articles with the tag "Reinforcement Learning".

• How OSWorld Turns Computer Work into an Agent Benchmark

  3 Oct, 2026 · 35 min read

  A technical guide to OSWorld's origins, 13 real tasks, Verified, 2.0 and 2.1, scoring contracts, and the experiments needed to distinguish local progress from complete computer work.

• How Should We Repair Reasoning Traces Before Distillation?

  1 Oct, 2026 · 23 min read

  A map of 26 research papers on reasoning distillation, trajectory correction, and student compatibility: where each method acts, what it costs to implement, and an experiment to test how much of an RL teacher trace to rewrite.

• What Makes an SFT Example Worth Learning? Quality, Novelty, and Learnability

  Updated: 27 Sep, 2026 · 23 min read

  From curriculum learning and DAgger to LIMA, LESS, and on-policy distillation: a source-backed framework for separating SFT data quality, novelty, and learnability, with executable examples and a small negative result.

• 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.