What Actually Happens When You Run This
Sf Duel Wonderland Training Stage 10 is essentially a checkpoint in a larger evaluation pipeline. You feed it samples, the model generates responses, and then a scoring function rates those outputs against a rubric that rewards coherence, factual grounding, and style compliance. That's the surface description. The thing nobody tells you is how brittle the scoring function gets once you push past Stage 7. I ran into a real problem last month when a batch of Stage 10 evaluations came back with wildly inconsistent scores on nearly identical inputs. The variance was like 18% between runs on the same data. Turns out the rubric weights shifted slightly between two versions of the scoring script, and nobody had bumped the version string in the config file. I caught it because one of my test cases kept flipping from a 7.2 to a 5.8 for no visible reason. The workaround was pinning the scoring dependency to the exact commit hash and adding a pre-flight check that diffs the rubric checksum against a known-good value before each run.
Sf Duel Wonderland Training Stage 10 Setup and Workflow
The setup itself is straightforward if you ignore the documentation, which hasn't been updated since Stage 8. You'll need a Python environment with at least version 3.10, and the repo pulls in a few heavy dependencies like torch and transformers. The install process takes about 12 to 18 minutes on a decent machine, longer if you're pulling weights from Hugging Face and your connection is mediocre. I usually pre-download the model checkpoints to a local cache directory so I'm not re-downloading 8 gigabytes every time I reset my environment. Once installed, you configure the stage by editing the YAML file in the config directory. The critical fields are the dataset path, the batch size, and the evaluation metric. Most people mess up the batch size setting. The documentation says any value from 1 to 64 works, but anything above 32 starts producing out-of-memory errors on GPUs with less than 24GB of VRAM, and the scores become unstable because the sampling temperature gets effectively rounded down during batching. I stick to 16. It's slower but the results are actually reproducible. There's a counter-intuitive thing about Stage 10 that trips up a lot of people running this for the first time. The training loop doesn't actually optimize the model weights during the evaluation phase. The name is misleading. What's happening is a pass-through inference scan where the model's existing weights are frozen and the outputs are just scored. If you're expecting gradient updates, you're going to be very confused when your loss curves look completely flat. The actual training happens in Stages 1 through 9. Stage 10 is purely diagnostic.
I learned that the hard way. Spent about three hours debugging why my validation loss wasn't moving, checking learning rates, scheduler configs, everything. Then I read the actual README more carefully and realized I'd been treating an evaluation stage like a training stage. Pretty standard beginner mistake honestly. The dataset format expects JSONL files with a specific schema. Each line needs a prompt field, a reference answer field, and an optional context field. If you're pulling data from another pipeline, you'll need to remap your columns. I wrote a quick conversion script that takes CSV exports from our internal data platform and reshapes them into the right format. It runs in about 45 seconds for a dataset of 5,000 samples on my machine. One edge case worth noting: the scoring function has a hard length cap on generated responses. If the model outputs more than 512 tokens, the score gets automatically truncated and penalized. Some newer models naturally generate longer, more detailed responses, so they actually score lower on Stage 10 than older models that are more concise. This isn't necessarily a reflection of quality. It's a reflection of the rubric design. If your use case values thoroughness over brevity, you'll want to adjust the length penalty parameter in the config, though the default value of 0.3 works reasonably well for most general-purpose tasks.
Get the Full Details
Performance wise, a full Stage 10 run on a dataset of 10,000 samples takes roughly 40 to 60 minutes on an A100 GPU. On a consumer card like a 4090, expect closer to two and a half hours. CPU-only execution is theoretically possible but I wouldn't recommend it. The inference throughput drops by a factor of about 20x and you're looking at something like eight to ten hours for the same dataset. The main limitation of this stage is that it only evaluates single-turn interactions. If your application involves multi-turn dialogues or agentic workflows with tool use, Stage 10 won't capture any of that complexity. It's a narrow slice of capability. There are plans to introduce a multi-turn variant in a future stage, but as of right now that doesn't exist. If you need multi-turn evaluation, you're better off running your data through a custom harness or waiting for the next release. Another honest downside is the scoring function's sensitivity to prompt formatting. Small changes in whitespace, capitalization, or instruction phrasing can shift scores by a point or two. This means you can't easily compare Stage 10 results across different prompt templates without normalizing for format first. I keep a standardized prompt template in a shared file and run all my comparisons through that. It removes a lot of the noise.
If Stage 10 isn't giving you the signal you need, the best alternative I've found is pairing it with a manual annotation pass on a sample of 200 to 300 examples. The automated scores will tell you the general direction, but human raters catch nuance the rubric misses, especially around reasoning quality and edge-case handling. It takes more time, maybe three to four hours for a small team, but the correlation between human ratings and actual production performance is significantly higher than Stage 10 scores alone. I've been running these evaluations regularly and the pipeline has been stable since I pinned the rubric version and locked the batch size. It's not glamorous work but it's the kind of thing that matters when you're trying to decide whether a model update actually improved things or just changed the noise pattern. Don't skip the diagnostic stages just because they feel less exciting than the training stages. They're usually where the real problems show up.
