Yifan Wu(@yifannnwu):Thanks @guohao_li for sharing—and for building SETA! It gave us a practical RL environment for training an open-weight memory policy with SFT + GRPO on Terminal Bench tasks. Also grateful to the open-source projects that made this possible: @harborframework for the agent harness and @rllm

2026.07.14 05:42

Thanks @guohao_li for sharing—and for building SETA! It gave us a practical RL environment for training an open-weight memory policy with SFT + GRPO on Terminal Bench tasks. Also grateful to the open-source projects that made this possible: @harborframework for the agent harness and @rllm_project for the training infrastructure.

Guohao Li 🐫@guohao_li

2026.07.13 12:39

"behavioral state decay": the failure mode that long-horizon agents forget what matters. meta ai's fix: a memory agent that updates the memory bank and then decides whether to emit a proactive intervention. they train qwen3.5-27b on seta using sft and grpo and show gains on terminal-bench 2.0. great to see seta terminal agent rl envs used this way - remember when it matters by meta ai @yifannnwu @zhuokaiz: - our seta project: