SourceStalecollected in 35m

Fix Qwen3.5 Overthinking with Budget Flags

PostLinkedIn
🦙Read original on Reddit r/LocalLLaMA
#reasoning-fix#inference-hack#qwen-tuningqwen3.5qwen3.5llama.cpp

💡End Qwen3.5 endless thinking loops with 2 simple llama.cpp flags

⚡ 30-Second TL;DR

What Changed

--reasoning-budget 4096 stops at token threshold

Why It Matters

Quick hack improves usability of Qwen3.5 in inference engines, preventing verbose outputs. Useful for real-time local deployments.

What To Do Next

Run llama-server with --reasoning-budget 1024 --reasoning-budget-message '. Okay enough thinking.' for Qwen3.5.

Who should care:Developers & AI Engineers

Key Points

  • --reasoning-budget 4096 stops at token threshold
  • --reasoning-budget-message appends 'Okay enough thinking'
  • Tested on Qwen3.5-35B-A3B Q4_K_M with ROCm
  • Lower budgets quicker but reduce answer quality

🧠 Deep Insight

Background and context from public sources — not the original article. 6 sources cited.

🔑 Enhanced Key Takeaways

  • Llama.cpp's reasoning budget uses a sampler mechanism to count tokens during reasoning phases and terminates generation upon reaching the set threshold, enhancing control over o1-like chain-of-thought processes[2].
  • The feature addresses KV cache inconsistencies in MLX on Mac, recommending llama.cpp over MLX for stable performance during branched conversations[1].
  • Recent llama.cpp updates fixed bugs in Qwen3-Coder-Next GGUF calculations that caused looping issues, requiring model re-downloads for compatibility[3].

🛠️ Technical Deep Dive

  • Reasoning budget implemented via sampler token counting in llama.cpp, replacing prior stub; enforces termination at budget limit like 4096 tokens[2].
  • Tested effectively on Qwen3.5-35B MoE variant (A3B active params), where budgets improved decision efficiency without quality loss[2][1].
  • ROCm support confirmed for AMD GPUs like Radeon 7900 XTX, though MXFP4 quantization limited to NVIDIA[1].

🔮 Future ImplicationsAI analysis grounded in cited sources

Llama.cpp reasoning budgets will standardize in local inference frameworks by mid-2026
Recent sampler-based implementation and successful Qwen3.5 tests indicate maturing feature adoption across backends like vLLM[2][3].
MoE models like Qwen3.5-A3B will see 20-30% inference speed gains with budget controls
User benchmarks show tuned setups achieving 27-29 t/s on Qwen3.5-122B, with budgets mitigating overthinking overhead[5].

Timeline

2026-02
Llama.cpp fixes Qwen3-Coder-Next GGUF calculation bug causing loops
2026-02
Llama.cpp improves tool-calling parsing for Qwen3 models
2026-03
Llama.cpp introduces full reasoning budget via sampler mechanism
📰

Weekly AI Recap

Read this week's curated digest of top AI events →

👉Related Updates

AI-curated news aggregator. All content rights belong to original publishers.
Original source: Reddit r/LocalLLaMA

This is a summary, not the original. Read the source, or get the weekly briefing.

The weekly digest

One email a week. Unsubscribe anytime.