Alternative for gradient computation in non-traditional environments
Problem
Commenters note that gradient computation is limited in non-traditional environments (e.g., RNNs without BPTT or where loss.backward cannot be called directly). Yet skepticism persists, regarding such methods' efficiency and practical impact compared to standard gradient descent.
Pain points
- Unable to directly call loss.backward for gradient computation when applying traditional differentiable neural networks to non-traditional environments
- Computationally inefficient, requiring thousands of forward passes for Monte Carlo gradient estimation, viewed by users as 'extremely not useful'
Questions to verify
- Have you encountered situations in real projects where backprop cannot be used?
- Would you consider using backprop-free optimization methods to solve such problems?
Evidence
quote · @匿名
Since reading that paper, I've had a lot of fun trying to apply this technique to settings I would describe as 'a traditional differentiable neural network being applied in a non-traditional environment where you can't just call loss.backward and hope for the best'.
quote · @匿名
This is super yawn-worthy. Instead of backprop for the exact gradient you can run forward passes a thousand times with perturbed weights and get a Monte Carlo estimate of the gradient. Not very clever. Extremely NOT useful.
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.