feat: support kilocode model calls through a Gym model server

[ananthsub]

Summary

The kilocode agent called its model provider directly, so its requests and responses never reached Gym. Model calls went uncaptured, the provider block had to be rewritten per backend, and there was no path to token IDs or logprobs.

This adds model_server to KiloCodeAgentConfig. When set, the agent writes a nemo provider into kilo.json pointed at that server and passes -m nemo/<model>, so one config runs against vLLM, OpenAI, or an inference provider by swapping --model-type. Setting model_server: null keeps the previous behaviour of calling a provider declared in kilo_config.

What changed

• model_server wiring: _write_kilo_config becomes build-then-write. It used to return early when kilo_config was empty, which would have left a model-server-only run with no provider at all. _build_command and _env now prefer the resolved URL, so a config carrying both a model server and openai_base_url does not point the subprocess environment at the provider the nemo provider is meant to replace.
• Per-rollout capture: During /run the base URL carries the /ng-rollout/<id> prefix, using the base class's url_path_for_run and resolve_model_base_url rather than a local reimplementation. Captured model calls are attributable to the rollout that made them.

On the model limits

context_window and max_output_tokens set kilo's limit. The defaults are sized for a 32k-window model server rather than copied from opencode_agent.

Kilo's system prompt and tool definitions run to ~10k tokens, so a large output budget pushes prompt + max_tokens past max_model_len. vLLM rejects that with a 400, which the Gym model server converts into an empty completion with finish_reason: length rather than an error (vllm_model/app.py, is_out_of_context_length). The run then produces no assistant message and scores zero, with nothing in the CLI's output to say why.

Two related notes:

• There is no truncating default to guard against. An unlisted model gets limit.output: 0, and kilo's min(limit.output, 32000) || 32000 falls back to 32000. The hazard is a budget that is too large, not too small.
• reasoning_field sets interleaved.field. Kilo turns interleaved reasoning off for custom openai-compatible providers unless the field is named (its built-in default only applies it to model ids containing "deepseek"), so without this the reasoning channel is dropped. It defaults to reasoning_content, which is what Gym model servers write; vLLM >= 0.16 also sends reasoning, which is why it is configurable rather than hardcoded.

Kilo splits -m on the first / only (let [A, ...L] = H.split("/")), so a slashed model name such as nemo/Qwen/Qwen2.5-7B-Instruct resolves to the model Qwen/Qwen2.5-7B-Instruct under the provider nemo. Verified against the shipped binary and live.

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