lucidrains
|
35c1db4c7d
|
sketch of training from sim env
0.0.69
|
2025-10-24 09:13:09 -07:00 |
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lucidrains
|
27ac05efb0
|
function for combining experiences
0.0.67
|
2025-10-24 08:00:10 -07:00 |
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lucidrains
|
d0ffc6bfed
|
with or without signed advantage
0.0.66
|
2025-10-23 16:24:29 -07:00 |
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lucidrains
|
fb3e026fe0
|
handle vectorized env
0.0.65
|
2025-10-22 11:19:44 -07:00 |
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lucidrains
|
7ecc5d03e8
|
wire up the time kv cache when interacting with sim / env
0.0.62
|
2025-10-22 08:39:11 -07:00 |
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lucidrains
|
d82debb7a6
|
first pass through gathering experience with a mock env for online rl
0.0.61
|
2025-10-22 08:32:46 -07:00 |
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lucidrains
|
03b16a48f2
|
sketch out the dream trainer, seems like they only fine tune the heads
0.0.60
|
2025-10-22 06:41:10 -07:00 |
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lucidrains
|
6f1a7a24ed
|
try to fix ci
|
2025-10-21 11:47:39 -07:00 |
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lucidrains
|
e316499047
|
naming
|
2025-10-21 10:57:55 -07:00 |
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lucidrains
|
40da985c6b
|
tweak bc trainer
0.0.59
|
2025-10-21 10:55:24 -07:00 |
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lucidrains
|
2fc3b17149
|
take a gradient step with behavioral clone trainer, make sure it works with and without actions and rewards
0.0.57
|
2025-10-21 10:20:08 -07:00 |
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lucidrains
|
283d59d75a
|
oops
|
2025-10-21 09:50:07 -07:00 |
|
lucidrains
|
4a5465eeb6
|
fix ci
|
2025-10-21 09:17:53 -07:00 |
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lucidrains
|
b34128d3d0
|
make sure time kv cache can be passed back in during generation
0.0.55
|
2025-10-21 09:15:32 -07:00 |
|
lucidrains
|
7ba3988fb9
|
prepare a mock for interacting with online env
|
2025-10-21 09:03:20 -07:00 |
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lucidrains
|
ea13d4fcab
|
take a gradient step with video tokenizer trainer
0.0.54
|
2025-10-21 08:52:22 -07:00 |
|
lucidrains
|
15876d34cf
|
more muon prep
0.0.53
|
2025-10-21 08:23:59 -07:00 |
|
lucidrains
|
b4763caff9
|
fix rotary embeddings in presence of kv caching
|
2025-10-21 07:10:21 -07:00 |
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lucidrains
|
7195bbb196
|
oops
0.0.50
|
2025-10-20 12:42:27 -07:00 |
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lucidrains
|
ca244a290c
|
first pass through the kv cache for the time block in the dynamics model
0.0.49
|
2025-10-20 12:25:50 -07:00 |
|
lucidrains
|
a7e0c395c3
|
allow for only rmsnorm for keys in attention
0.0.48
|
2025-10-20 11:20:49 -07:00 |
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lucidrains
|
1345326656
|
another measure for the attending to nothing issue
0.0.47
|
2025-10-20 10:32:31 -07:00 |
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lucidrains
|
55574c054e
|
assert
0.0.46
|
2025-10-19 09:59:42 -07:00 |
|
lucidrains
|
27ed6d0ba5
|
fix time kv cache
0.0.45
|
2025-10-19 09:16:06 -07:00 |
|
lucidrains
|
4930002e99
|
bit of progress on time kv cache
0.0.44
|
2025-10-19 09:04:26 -07:00 |
|
lucidrains
|
ecbe13efe8
|
allow for setting different loss weights for each MTP head (perhaps more weight on the next vs some far out prediction)
0.0.43
|
2025-10-19 08:37:56 -07:00 |
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lucidrains
|
f651d779e3
|
able to control the update of the loss ema from dynamics model forward
0.0.42
|
2025-10-19 08:25:50 -07:00 |
|
lucidrains
|
374667d8a9
|
take care of the loss normalization mentioned at the end of the first paragraph of section 3
0.0.41
|
2025-10-19 08:24:41 -07:00 |
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lucidrains
|
79a1b1c46e
|
oops
0.0.40
|
2025-10-18 10:31:48 -07:00 |
|
lucidrains
|
b6aa19f31e
|
complete multi-token prediction for actions, tackle loss balancing another day
0.0.38
|
2025-10-18 10:23:14 -07:00 |
|
lucidrains
|
bc629d78b1
|
inverse norm for continuous actions when sampling
0.0.37
|
2025-10-18 08:55:04 -07:00 |
|
lucidrains
|
0ee475d2df
|
oops
0.0.36
|
2025-10-18 08:50:53 -07:00 |
|
lucidrains
|
8c88a33d3b
|
complete multi token prediction for the reward head
0.0.35
|
2025-10-18 08:33:06 -07:00 |
|
lucidrains
|
911a1a8434
|
oops
0.0.34
|
2025-10-18 08:07:06 -07:00 |
|
lucidrains
|
5fc0022bbf
|
the function for generating the MTP targets, as well as the mask for the losses
|
2025-10-18 08:04:51 -07:00 |
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lucidrains
|
83cfd2cd1b
|
task conditioning when dreaming
0.0.33
|
2025-10-18 07:47:13 -07:00 |
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lucidrains
|
22e13c45fc
|
rename
0.0.32
|
2025-10-17 14:44:25 -07:00 |
|
lucidrains
|
c967404471
|
0.0.31
0.0.31
|
2025-10-17 08:55:42 -07:00 |
|
lucidrains
|
0c1b067f97
|
if optimizer is passed into the learn from dreams function, take the optimizer steps, otherwise let the researcher handle it externally. also ready muon
|
2025-10-17 08:55:20 -07:00 |
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lucidrains
|
cb416c0d44
|
handle the entropies during policy optimization
0.0.30
|
2025-10-17 08:47:26 -07:00 |
|
lucidrains
|
61773c8219
|
eventually we will need to learn from the outside stream of experience
0.0.29
|
2025-10-17 08:06:24 -07:00 |
|
lucidrains
|
0dba734280
|
start the learning in dreams portion
0.0.27
|
2025-10-17 08:00:47 -07:00 |
|
lucidrains
|
a0161760a0
|
extract the log probs and predicted values (symexp two hot encoded) for the phase 3 RL training
0.0.26
|
2025-10-16 10:40:59 -07:00 |
|
lucidrains
|
2d20d0a6c1
|
able to roll out actions from one agent within the dreams of a world model
0.0.25
|
2025-10-16 10:15:43 -07:00 |
|
lucidrains
|
d74f09f0b3
|
a researcher in discord pointed out that the tokenizer also uses the axial space time transformer. redo without the 3d rotary and block causal, greatly simplifying the implementation
0.0.24
|
2025-10-16 09:40:14 -07:00 |
|
lucidrains
|
2ccb290e26
|
pass the attend kwargs for the block causal masking in tokenizer
0.0.23
|
2025-10-16 08:33:26 -07:00 |
|
lucidrains
|
517ef6b94b
|
oops
0.0.22
|
2025-10-16 07:03:51 -07:00 |
|
lucidrains
|
ec18bc0fa4
|
cleanup
|
2025-10-16 06:44:28 -07:00 |
|
lucidrains
|
2a902eaaf7
|
allow reward tokens to be attended to as state optionally, DT-esque. figure out multi-agent scenario once i get around to it
0.0.21
|
2025-10-16 06:41:02 -07:00 |
|
lucidrains
|
d28251e9f9
|
another consideration before knocking out the RL logic
0.0.20
|
2025-10-14 11:10:26 -07:00 |
|