How We Stopped Laguna S 2.1 From Looping Forever on Our DGX Spark

We recently deployed Poolside's Laguna S 2.1 (118B MoE) on a DGX Spark. The model is impressive — 90% on our HumanEval-inspired coding benchmark, beating most cloud models in its weight class. But there was a catch: 40% of runs on certain problems never finished.

Not "took too long." Never. The model would enter a reasoning spiral — burning 50,000+ tokens, hitting HTTP timeouts at 30 minutes, consuming a full hour of compute for two runs that went nowhere.

Here's exactly what was happening, how we diagnosed it, and the two-line vLLM config that fixed it.

The Symptom: Reasoning That Never Converges

Laguna S 2.1 is a reasoning model. Before producing code, it thinks — often extensively. This is fine. What's not fine is when the thinking loops.

Take make_palindrome, a medium-difficulty coding problem (find the shortest palindrome starting from a given string). With the default vLLM deployment:

Run 1: ✅ PASS —  374s, 13,033 tokens
Run 2: ❌ TIMEOUT — 1,800s, 0 tokens (loop)
Run 3: ❌ TIMEOUT — 1,800s, 0 tokens (loop)
Run 4: ✅ PASS —  187s,  7,260 tokens
Run 5: ✅ PASS —  215s,  7,710 tokens

Two out of five runs looped indefinitely. That's 3,600 seconds — one full hour — of GPU time producing nothing.

The Root Cause: Deterministic Traps Without Stochastic Escape

Reasoning models, especially MoE architectures like Laguna, can fall into what we call a reasoning attractor — a region of token-space where every plausible next token leads back to the same semantic loop. The model isn't "broken." It's just that the probability landscape for certain prompts creates a basin with no exit.

At temperature = 0 (greedy decoding), this is fatal. The model picks the most likely token every time, marches into the basin, and never escapes.

At temperature = 1.0 (the official checkpoint default for the NVFP4 variant), the model can escape stochastically — which is why some runs pass. But the variance is enormous. One run finishes in 187 seconds. The next one spirals for 30 minutes.

The Fix: Two Lines in Your vLLM Config

The solution is to give vLLM two tools it already has:

1. Repetition Penalty

repetition_penalty: 1.15

This applies a small penalty each time a token reappears. It's subtle enough not to hurt normal reasoning (the model should revisit concepts as it refines), but strong enough to break tight loops. We validated 1.15 against community reports on the exact same hardware configuration (DGX Spark, NVFP4 checkpoint).

2. Native N-Gram Loop Detection

repetition_min_pattern_size: 8
repetition_max_pattern_size: 24
repetition_min_count: 3

This is vLLM's built-in repetition detection (≥ v0.17.0). It monitors the generated stream for n-grams of [8–24] tokens that appear ≥ 3 times. If detected, vLLM kills the generation at the scheduler level — no more waiting for an HTTP timeout.

The parameters are calibrated to match our earlier FastAPI-based loop detector, which we validated against hours of Laguna reasoning logs:

• 8–24 tokens ≈ 12 words (catches both verbatim and reformulated loops)
• Threshold of 3 (the same n-gram must appear 3 times in close proximity)
• Zero false positives on convergent reasoning (tested on 102K-character legitimate reasoning traces)

Full vLLM Override

--override-generation-config '{
  "temperature": 0.7,
  "top_p": 0.95,
  "repetition_penalty": 1.15,
  "repetition_detection": {
    "min_pattern_size": 8,
    "max_pattern_size": 24,
    "min_count": 3
  }
}'

We also switched from temp = 0 to temp = 0.7 with top_p = 0.95. This keeps outputs diverse enough to avoid the deterministic trap zone, while staying within Poolside's recommended sampling range.

The Result

Same problem, same hardware, five fresh runs:

Run 1: ❌ (logic error, not a loop) —  362s,  8,479 tokens
Run 2: ✅ PASS —                       712s, 16,986 tokens
Run 3: ✅ PASS —                       489s, 11,501 tokens
Run 4: ✅ PASS —                       502s, 12,352 tokens
Run 5: ✅ PASS —                       517s, 12,376 tokens

The average time increased — from 259s to 516s. This is actually a good sign. The old recipe's "fast" runs were lucky escapes from the attractor basin. The new recipe lets the model reason properly, exploring branches until it converges — without ever spiraling. The repetition_detection catches the spirals; the repetition_penalty prevents them from starting.

Zero timeouts. Zero loops. One hour of wasted compute eliminated.

What We Learned

1. Reasoning models need an escape hatch. Greedy decoding + complex prompts = attractor basins with no exit. Repetition penalty is your stochastic escape hatch.
2. vLLM's native repetition detection works. You don't need a custom proxy with n-gram loop detection (we built one — it worked, but vLLM's built-in is simpler and runs at the scheduler level).
3. Don't trust the checkpoint defaults blindly. The official config says temp = 1.0, top_k = 20. It works — but on a DGX Spark with NVFP4 quantization, temp = 0.7, top_p = 0.95 is more stable. Test on your hardware.
4. The "long generation = loop" heuristic is wrong. Some of our successful runs took 12 minutes and 17,000 tokens. They weren't looping — they were reasoning thoroughly. vLLM's n-gram detection distinguishes true loops from long reasoning. Don't just set a low timeout and call it a day.

Try It Yourself

Full vLLM launch command for DGX Spark + Laguna S 2.1 NVFP4:

vllm serve poolside/Laguna-S-2.1-NVFP4 \
  --served-model-name poolside/Laguna-S-2.1 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --max-num-seqs 32 \
  --gpu-memory-utilization 0.85 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --trust-remote-code \
  --override-generation-config '{
    "temperature": 0.7,
    "top_p": 0.95,
    "repetition_penalty": 1.15,
    "repetition_detection": {
      "min_pattern_size": 8,
      "max_pattern_size": 24,
      "min_count": 3
    }
  }'

That's it. Two parameters. One hour of compute saved per deployment.

Navis AI is an AI research lab based in Monaco, building on-premise AI appliances. We benchmark and deploy open-weight models on local hardware. Follow us for more inference engineering battle stories.