Check out TRACE, a new self-improvement approach where the agent identifies the missing capabilities...
TL;DR · AI 摘要
Stanford AI Lab on X: "Check out TRACE, a new self-improvement approach where the agent identifies the missing capabilit...
核心要点
- 主题聚焦：Check out TRACE, a new self-improvement approach
- 来源：Stanford AI Lab(@StanfordAILab)，建议结合原文判断细节。
- AI 分析暂不可用，本条为保底评分与摘要。
Stanford AI Lab on X: "Check out TRACE, a new self-improvement approach where the agent identifies the missing capabilities behind its own failures and trains itself to address them. TRACE-trained Qwen3.6-27B reaches 73.2% on SWE-bench Verified, outperforming much larger models like Codex 5.2 and" / X
Stanford AI Lab
@StanfordAILab
Check out TRACE, a new self-improvement approach where the agent identifies the missing capabilities behind its own failures and trains itself to address them. TRACE-trained Qwen3.6-27B reaches 73.2% on SWE-bench Verified, outperforming much larger models like Codex 5.2 and GLM 5, while beating GRPO and GEPA with <1/4 the training rollouts. Exciting work led by
@
hangoo_kang
and
TarunSures41845
!
Hangoo Kang @ ICML ✈️
@hangoo_kang
Jul 7
“TRACE: Capability-Targeted Agentic Training” got Spotlight @ ICML AIWILD 🎉 Beats direct RL, GEPA, & synthetic-agent data on SWE-Bench Verified and τ²-Bench. TRACE-Qwen3.6-27B tops GPT-5.2-Codex, GLM 5, & Claude 4.5 Sonnet on SWE-Bench. Co-led with
. Thanks to
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11:45 PM · Jul 9, 2026
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