fix: don't pass task stop sequences to vLLM for reasoning models

[jwmacd]

When think_end_token is set, task-level stop sequences like "\n\n" (the fewshot delimiter default) fire inside blocks and truncate generation before any response is produced.

Two changes:

1. vllm_causallms.py: When think_end_token is set, only pass EOS to vLLM's SamplingParams. Task stop sequences remain in the cached gen_kwargs for post-processing.
2. utils.py: Reorder postprocess_generated_text to strip thinking content before applying stop sequences, so stops match the actual response rather than the reasoning trace.

Non-reasoning models are unaffected — the code path only diverges when think_end_token is set.

Scope

This affects all generate_until tasks. 17 tasks in the repo don't specify explicit until in their generation_kwargs and inherit the
fewshot delimiter (typically "\n\n") as a stop sequence. Any reasoning model evaluated on these tasks may produce truncated or empty output without this fix.

Test results

Tested with Kimi-K2.5 (MoE, reasoning_parser=kimi_k2) on JSONSchemaBench (generate_until task, default until: ["\n\n"] from fewshot delimiter, max_model_len=65536, max_gen_toks=32768):

┌────────────────────┬─────────┬───────────┬─────────┐
│ │ JS-Easy │ JS-Medium │ JS-Hard │
├────────────────────┼─────────┼───────────┼─────────┤
│ Before (unpatched) │ 0.00 │ 0.00 │ 0.00 │
├────────────────────┼─────────┼───────────┼─────────┤
│ After (patched) │ 0.99 │ 0.96 │ 0.88 │
└────────────────────┴─────────┴───────────┴─────────┘

Before: all 1,531 samples scored 0.0 on both json_validity and schema_compliance — generation was truncated inside blocks before any JSON was produced. Total eval time 778s (near-immediate truncation per sample).

After: real scores, 34 min eval time with full thinking traces and JSON output.

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