Training models to explain their own failures, then rewarding the explanation
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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. 🔗
Context
WRITER's self-reflection method trains a model to produce explanations of failure that help it succeed on a retry. A task checker first identifies an unsuccessful attempt. The model then writes a reflection and tries again with that reflection in . If the retry succeeds, the training update rewards the reflection text. The paper tests this on tool-call construction and arithmetic-equation tasks, where success can be checked against explicit rules or reference answers. The method ties an explanation's value to whether it helps solve the task, rather than simply whether it sounds convincing.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
- tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
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