LLaDA 2.2 claims 703.82 TPS on BFCL-V4 and 519 TPS on SWE-bench Verified
FellMentKE · x · 2026-07-24
LLaDA 2.2 pushes diffusion LLMs toward agent use
The post says LLaDA 2.2 is an agent-oriented MoE diffusion LLM whose key improvement is self-correction during decoding via Levenshtein editing — deletion, insertion, and substitution. The claim is that for agents, the real bottleneck was error accumulation, not block-parallel decoding itself.
Reported performance
- 703.82 TPS on BFCL-V4 function calling
- 519.0 TPS on SWE-bench Verified coding
- 1.64× BF16 throughput versus autoregressive models
It is positioned as a high-throughput, low-latency engine for AI agents, with links to a GitHub repo, Hugging Face, and a technical report.
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