reflection.md

title	status	authors	based_on	category	source	tags	slug	id	summary	updated_at
Reflection Loop	established	Nikola Balic (@nibzard)	Shinn et al. (2023)	Feedback Loops	https://arxiv.org/abs/2303.11366	self-feedback iterative-improvement evaluation	reflection	reflection-loop	Generative models may produce subpar output if they never review or critique their own work.	2026-01-05

Problem

Single-pass generation frequently misses edge cases, constraints, or quality criteria that become obvious on review. Without a structured revision loop, agents return the first plausible answer even when a better answer is reachable with lightweight critique.

Solution

After generating a draft, run an explicit self-evaluation pass against defined criteria and feed the critique into a revision attempt. Repeat until the output clears a threshold or retry budget is exhausted.

Use stable scoring rubrics (correctness, completeness, safety, style) so the loop improves objective quality rather than free-form restyling. For reduced bias, use a separate model for critique (dual-model architecture) at the cost of additional compute.

for attempt in range(max_iters):
    draft = generate(prompt)
    score, critique = evaluate(draft, metric)
    if score >= threshold:
        return draft
    prompt = incorporate(critique, prompt)

How to use it

Use this when quality must meet explicit criteria in writing, reasoning, or code generation. Keep loop budgets small (2-3 iterations are typically optimal; beyond 3 shows diminishing returns), and log score deltas to verify that extra iterations are producing measurable gains.

Trade-offs

• Pros: Improves outputs with little supervision.
• Cons: Extra compute; may stall if the metric is poorly defined.

References

• Self-Refine: Improving Reasoning in Language Models via Iterative Feedback
• Reflexion: Language Agents with Verbal Reinforcement Learning (NeurIPS 2023) - adds episodic memory for persistent learning across trials