February 21, 2026
No rewind, just rage
Forward propagation of errors through time
Teaching AI without hitting rewind has the internet split—genius idea, messy reality
TLDR: Researchers proved you can train a sequence model by pushing errors forward instead of rewinding, but it falls apart when the model forgets and numbers go unstable. Comments split between “bold idea for future hardware” and “pretty but brittle,” with big praise for sharing a clear negative result.
Internet brainiacs just tried teaching looping AI without rewinding the tape. Instead of Backpropagation Through Time (BPTT)—the classic “go backward through a sequence to learn”—they push error signals forward. The math checks out, and in small demos it worked. Then reality bit: when the model starts “forgetting,” the numbers wobble and crash. Think “perfect recipe, bad oven.” Hardware fans cheered—no rewind means less memory and maybe great for analog chips and brain-inspired setups—but the party cooled fast.
Comments exploded. One camp hailed the authors for sharing a negative result in public: “More science, less hype.” Another camp rolled eyes: “clever idea, cruel arithmetic,” claiming computers don’t like this kind of number juggling. Memes flew: cassette rewind GIFs, “reverse UNO on backprop,” and “Schrödinger’s gradient—exists until rounding.” Optimists argued better number tricks or higher precision could save it; skeptics said you can’t outsmart forgetting. A spicy side debate: does this hint at how real brains learn? Replies: “maybe in biology, not on GPUs.” Regardless, the crowd loved the transparency and the math tour—complete with reasons why the team stopped. Catch the full write-up in the post.
Key Points
- •The article derives an exact forward-in-time error propagation algorithm for RNN training that reconstructs gradients without reverse-time processing.
- •A warm-up phase is used to establish initial conditions needed to compute exact gradients forward through the sequence.
- •Experiments show the method can train deep RNNs on non-trivial tasks but suffers from severe numerical instability in forgetting regimes.
- •Instability arises from floating-point arithmetic limitations despite mathematically correct derivations.
- •The method could reduce memory demands and aid neuromorphic or analog hardware, but its instability led the authors not to pursue it further.