# Banger paper from Tencent on environment evolution. Environment supply is becoming the main limit o…

- 来源：elvis (@omarsar0)
- 发布时间：2026-09-05 02:00
- AIHOT 分数：48
- AIHOT 链接：https://aihot.virxact.com/items/cmtn9rouk0tkcromy8isabnln
- 原文链接：https://x.com/omarsar0/status/2095934982363787373

## 正文

Banger paper from Tencent on environment evolution.

Environment supply is becoming the main limit on agent RL. So this is worth a read.

(bookmark it)

Agent RL needs a steady supply of environments hard enough to teach something new. Recent methods build them from the weaknesses an agent shows during its own rollouts.

That approach has two problems. The environments inherit the agent's blind spots, so they generalize poorly. And as the agent improves it exposes fewer weaknesses to build from, so the learning signal weakens.

Environment evolution raises difficulty without watching the agent at all. The paper derives three ways to make an environment harder straight from the multi-turn training objective, then applies them generation by generation on a fixed schedule.

They test the generator before trusting it. Hy4 preview, Claude Opus 5 and GPT-5.6 Sol all do worse on the evolved environments, which confirms the difficulty.

Plain long-horizon RL on Qwen3.6-27B and Qwen3.6-35B-A3B then adds 14.4 and 18.0 points on Terminal-Bench 2.1.

Paper: https://academy.dair.ai/papers/environment-evolution-for-terminal-agents-2609.04128