I Added Hindsight Memory to an ML Agent. F1 Didn't Move.
I expected the agent with memory to win. After 120 training runs on a churn-prediction task, the mean test F1 was 0.573 for the agent without memory and 0.574 for the one with it. The difference showed up somewhere else, and one of my own bugs nearly hid it. This is what I built, how I used Hindsight, and what the numbers did and didn't show. The problem I wanted to fix Training a model means…
The researcher expected the ML agent with added memory to outperform the baseline agent. However, the difference in performance (mean test F1 scores of 0.573 for baseline and 0.574 for memory agent) was negligible, not exceeding 0.001. The memory agent made fewer "bad runs" (4 out of 40 in sessions 2 and 3) compared to the baseline agent (9 out of 40), but the gap could be attributed to chance due to limited trials.
The researcher aimed to test whether memory would help the agent drop a problematic column (customerID) that causes overfitting. While the memory agent dropped the column in 52 out of 60 runs, the baseline agent also dropped it in all 60 runs, indicating the language model already knew to drop the column. A bug in the code replaced random configurations when the LLM call failed due to rate limits, introducing variability in results.
After fixing the bug, the memory agent's performance disappeared, highlighting the importance of accurate data collection. The researcher concluded that memory had little impact on the agent's performance and emphasized the need for transparent data collection and interpretation.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
