Bulk-filtering synthetic RL envs reveals Qwen's 'attempt the impossible first' disposition
kalomaze · x · 2026-09-17
kalomaze shares a behavioral finding from bulk-filtering synthetic RL environments (envs Qwen fails near-deterministically but GLM5.3 solves well). He hypothesizes Qwen was trained in environments that sometimes accept impossible results alongside decent ones that reject them, leaving room for the policy to learn 'attempt the obviously impossible first' as a general disposition — a cautionary tale about how synthetic env quality silently shapes model behavior.
Related event: Researcher Mines RL Environments Where Qwen Fails and GLM5.3 Succeeds(2 posts)→
More from Research
- CHOP uses AI to cut children's heart 3D modeling from 4 hours to seconds — kimmonismus · 2026-09-17
- Liouville Goldbach Conjecture Fully Proven and Lean-Verified, Code Released on GitHub — ctjlewis · 2026-09-17
- RIGOR paper turns 360° images into four virtual rigs for omnidirectional reconstruction — kwangmoo_yi · 2026-09-17
- Measuring spec ambiguity as a predictor of correlated failure across model families — breadstickdingdong · 2026-09-17
- Retraining fp16 scales only: patching a 3-bit Qwen3.8-27B GGUF closer to its BF16 parent — ZenZombie117 · 2026-09-17
- Indie brainstorm independently converges on Vals AI's new multi-agent benchmark — are we mode collapsing? — scaling01 · 2026-09-17