{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "infermatrixlab-vllm-rlt--compare-fixed-depth-vs-early-exit",
 "repo": "InferMatrixLab/vllm-rlt",
 "repo_url": "https://github.com/InferMatrixLab/vllm-rlt",
 "repo_slug": "infermatrixlab-vllm-rlt",
 "title": "Compare fixed-depth and early-exit decoding on Ouro",
 "page_title": "How to compare fixed-depth and early-exit decoding on Ouro with vLLM RLT",
 "summary": "Measure how loop depth settings change Ouro-1.4B outputs for a chosen set of prompts.",
 "who": "Research engineer studying adaptive computation in looped transformers who needs side-by-side runs on the same prompts.",
 "problem": "Early-exit decoding saves compute only if the outputs stay acceptable, and the team needs evidence for their own prompts.",
 "outcome": "A small table of outputs and loop-depth behavior for fixed-depth and adaptive exit runs, plus a note on where the results differ.",
 "steps": [
  "Run `uv pip install -e .` from the repository root.",
  "Read the Ouro runtime notes in docs/cdb_runtime.md and the GSM8K evaluation guide in docs/accuracy.md to choose a fair test set.",
  "Write a short Python script with the Python API that runs the same prompts once with fixed-depth decoding and once with Ouro's learned early-exit gate. (inferred, check the README)",
  "Set the per-request loop bound and exit threshold for each run and record the outputs.",
  "Compare the two result sets and note where outputs or depths diverge."
 ],
 "agent_prompt": "In the vllm-rlt repository, use the Python API to run Ouro-1.4B on the prompts in <prompt file> twice, once with fixed four-loop decoding and once with the early-exit gate. Output a table with prompt, both answers, and the loop depth used. Verify that both runs use the same prompts and the same decoding settings except for the exit policy.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "120 minutes",
 "basis": "readme",
 "caveat": "The README notes that output and exit-depth differences remain unresolved in some configurations, so treat any comparison as specific to your prompts and settings.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Linux machine with an NVIDIA GPU and a CUDA-enabled PyTorch installation compatible with the hardware",
  "Python 3.10 or newer and PyTorch 2.5 or newer",
  "uv installed to run the install command from the repository root",
  "Access to download the chosen checkpoint, such as ByteDance/Ouro-1.4B"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/compare-fixed-depth-vs-early-exit/",
 "md": "https://dedupedai.com/use-cases/infermatrixlab-vllm-rlt/compare-fixed-depth-vs-early-exit/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/infermatrixlab-vllm-rlt--compare-fixed-depth-vs-early-exit.json",
 "api_version": "v1"
}