AI Dataset2026
Provides live Codex-CLI agent run traces from GPT-5.6 Sol capturing coding, debugging, security reviews, and harness/seed workflows in cumulative next-action prefixes — suitable for supervised fine-tuning and analysis of tool-using coding agents.
AI Dataset2026
Provides verified, model-attested end-to-end agent coding and debugging trajectories (JSONL). Each whole-session trace was produced by moonshotai/kimi-k3 on the pi/openrouter runtime, passed acceptance tests and independent model screening — useful for SFT, distillation, and analyzing tool-use behavior.
AI Dataset2026
Provides newline-delimited JSON agent session traces (5 files) generated with Teich for moonshotai/kimi-k3, including recovered and embedded tool-schema snapshots so traces remain training-ready even when tools weren't invoked; includes guidance for Teich data preparation and conversion.
Presents a unified black-box reinforcement learning framework to train and optimize agents running inside complex execution harnesses. Uses sandbox-parallel rollouts, a serving proxy that captures model calls and reconstructs multi-turn trajectories as prefix trees, and adapted GRPO/PPO optimizers to achieve stable, scalable RL across heterogeneous harnesses.
AI Agent Papers2026
Finds token- and API-cost-saving harness mechanisms for long-horizon coding agents using automated recursive self-improvement; packages four surviving mechanisms (action fusion, context compaction, observation archiving, delegated reading) to cut recorded token traffic ~44.7–49.0% and API cost by about one third while preserving most capability.