Large Reasoning Model (LRM) Paradigm
Route reasoning-heavy tasks to a reasoning-tuned model that trades inference time for deliberation, rather than to a fast LLM that exhibits premature-closure.
Problem
Routing every task to a fast LLM means constraint-heavy tasks fail in characteristic ways (premature-closure, false-confidence-syndrome). Routing everything to an LRM is slow and expensive for easy tasks. The team needs a routing decision.
Solution
Build a router that classifies tasks: simple lookups / generation → LLM; multi-step math, formal reasoning, interconnected-constraint problems → LRM. Track per-class success rate to refine routing. Pair with complexity-based-routing, multi-model-routing, test-time-compute-scaling, generate-and-test-strategy, golden-rule-simpler-is-better (don't overuse LRM).
When to use
- Mixed task workload with both simple and constraint-heavy queries.
- Latency budgets allow LRM on some queries.
- Cost difference is bearable for the hard-task minority.
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