feat(reward-seat): move Skywork reward seat from fv-ml1 to esh-ml1; audit finds nothing superseding it

- Audit: Skywork-Reward-V2-Llama-3.1-8B is still #1 of 188 on AllenAI's
  RewardBench 2 per-sample results; no Skywork V3; the -40M sibling is
  vendor-marked experimental. Our AWQ W4A16 quant: 0.847 vs published bf16
  0.860 on a 150-prompt sample (within +/-2.9 pt SE), 96.2% pairwise
  agreement. Double BOS from vLLM on pre-templated text costs a further
  ~2.7 pts; callers must send add_special_tokens=false.
- No working consumer: 0 requests since 2026-09-13; Worldtree Domari points
  at a dead IP with a non-vLLM schema (reported to worldtree-dev).
- Move: sha256-identical model copy; vLLM v0.24.0 on esh-ml1 :8003 at 0.55
  util (KV 1.30x of a 16k request). Parity vs fv-ml1: 149/150 verdicts,
  99.8% pairwise signs, raw |delta| median 0.049.
- Gateway /scalar-judge passthrough -> 10.0.50.80:8003; fv-ml1 vllm-reward
  removed (~10.2 GB freed on GPU 1). stacks/vllm now holds only vllm-coder.

servers/esh-ml1/README.md

@@ -32,9 +32,14 @@ vLLM here after a bake-off; see
 | `tei-embed` | `Qwen/Qwen3-Embedding-0.6B` | 8001 | `qwen3-embedding` (`hosted_vllm/`, `/v1`) |
 | `tei-rerank` | `BAAI/bge-reranker-v2-m3` | 8013 | `reranker`, `reranker-a3-bge-v2-m3` (`huggingface/`, no `/v1`) |
 
-VRAM ~2.6 GB for both; the rest of the 16 GB is free for further utility seats.
-The vLLM image (`vllm/vllm-openai:v0.24.0`, ~30 GB) is still on disk: kept in
-case a vLLM-only seat (the reward classifier) moves here.
+VRAM ~2.6 GB for both.
+
+**Also here since 2026-09-25: the reward seat** — `stacks/reward-seat`,
+`vllm-reward` (Skywork-Reward-V2-Llama-3.1-8B AWQ, vLLM v0.24.0) on `:8003`, behind
+the gateway passthrough `/scalar-judge`. It is the one vLLM seat on the box,
+because TEI cannot serve a Llama classifier. Audit and parity:
+[`stacks/reward-seat/README.md`](../../stacks/reward-seat/README.md). GPU total
+with all three: ~10.4 of 16.4 GB.
 
 **Cut-over verified 2026-09-25** through the gateway against fv-ml1's vLLM seats
 just before they were retired: embed cosine median 0.999927 / min 0.999881