Reflexion Retry Decision
Problem Statement
In a Reflexion loop, the agent retries a task after reflecting on failures, but only while it has retries left and the evaluation score is improving. Decide whether to attempt another try.
Background
Given the current attempt number (1-indexed), a max_attempts cap, the list of scores observed so far (one per attempt, higher is better), and a target threshold: retry only if the latest score is below threshold, attempts remain, and the latest score strictly improved over the previous attempt (or it is the first attempt). No improvement means reflection isn't helping — stop.
Your Task
def should_retry(scores, attempt, max_attempts, threshold):
Return True to retry, else False.
Input Format
- scores (list of floats, len == attempt), attempt (int), max_attempts (int), threshold (float).
Output Format
- A boolean.
Sample
print(should_retry([0.4, 0.6], 2, 4, 0.9))
Output:
True
Example:
print(should_retry([0.4, 0.6], 2, 4, 0.9))
True
- Extract the most recent performance metric from the score history to evaluate current standing: latest=0.6.
- Verify if the agent has met the success criteria by comparing the latest score against the target: since 0.6<0.9, the performance is insufficient, so the retry process continues.
- Check if the attempt limit has been reached to ensure resources remain: with attempt=2 and max_attempts=4, the condition 2<4 holds, indicating retries are still permitted.
- Determine if the reflection strategy is effective by checking for strict improvement over the previous attempt: comparing the latest score 0.6 to the prior score 0.4, we see 0.6>0.4, confirming positive progress.
- Since the score is below the threshold, attempts remain, and performance is improving, the decision is made to proceed with another try.
- The final output is True
Constraints:
- Retry requires: latest score < threshold,
attempt < max_attempts, and improvement over previous (first attempt counts as improving). scoreshas one entry per attempt so far.- Return a bool.
1. Background Knowledge
Reflexion is a self-improvement paradigm for AI agents where the system generates a natural-language reflection after a failed or suboptimal attempt, then uses that reflection to guide subsequent trials. The core loop is: act → evaluate → reflect → retry. This problem models the control gate of that loop: deciding whether another iteration is worthwhile before committing compute resources.
The decision hinges on three independent signals:
- Budget: the agent has a hard cap (max_attempts) on how many times it may try.
- Progress: the most recent score must be strictly higher than the previous score. If the score is flat or declining, reflection is not helping and further retries are unlikely to succeed.
- Goal distance: the latest score must still be below the target threshold. If it already meets or exceeds the threshold, the task is effectively solved and no retry is needed.
All three conditions must hold simultaneously (logical AND) for a retry to be granted. This mirrors real-world agent frameworks (e.g., LangChain, AutoGen) where a policy function gates each loop iteration.
2. Algorithm Approach
This is a multi-condition boolean gate. There is no search, sorting, or dynamic programming—just a constant-time evaluation of three predicates:
- attempt < max_attempts (budget remains)
- scores[-1] < threshold (goal not yet met)
- attempt == 1 or scores[-1] > scores[-2] (strict improvement, or first attempt where no prior score exists)
Return True only if all three are satisfied; otherwise False.
3. Step-by-Step Strategy
Continue the full explanation
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