Large language models have come a long way – but they still stumble when good training data is scarce.
At WRITER, we’ve been testing a different path: self-reflection. 🧠
Instead of just feeding models more data, we train them to pause, ask why they failed, and then retry with the benefit of that insight. When the retry works, the reflection itself gets reinforced, teaching the model better reasoning patterns over time.
The results were surprising:
💡 Smaller fine-tuned models beat much larger ones on tough tasks like function calling and math problem-solving.
💡 They held their own on broad benchmarks, too.
Self-reflection isn’t just a fix for specific failures. It’s a meta-skill – a way for models to internalize reasoning strategies that carry across challenges.
WRITER’s self-reflection research points toward models that perform well and can evolve through introspection.
Read the full breakdown from Shelly Bensal here.
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tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
Reinforcing a model's own failure analysis, rather than adding more training data, is a workable option when task-specific data is scarce, and the reported gains land on tool useA model's ability to call external functions — run code, search the web, edit files — instead of only generating text.Full definition →, the part AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → builders depend on.
