The training data contains roughly 9,238 sequence users and 8,456 target users.
Training yields about one million examples per epoch. Validation tracks organic 80% listens and likes from the validation window, excluding all training encounters. The task is discovery: recommend unseen tracks.
Many strong listens may be ineligible if they repeat known tracks. I will check the true number of eligible relevant items and whether popular tracks dominate labels. Preparing data streams for 43 million listens could take a few minutes, so I will copy the code to /app/solution and start caching while implementing improvements.
The diagnosis centers on the training objective's use of negative examples. Uniform negatives are too easy: with thirty thousand items, random negatives are almost always irrelevant, so BCE gradients vanish. In-batch sampling also wastes natural hard negatives by limiting each batch to only eight unique items.
Independent BCE does not create competition between negatives. The full-history false-negative filter is correct for discovery and should be kept. The current in-batch propensity correction clips weights to [0.25, 4], which downweights popular items. For tail discovery, popular tracks should instead be harder negatives. There is no popularity-aware negative sampling.
Dislikes are rare but high-quality signals that currently receive no negative supervision. The 32-item sample size is too small for a thirty-thousand-item catalog; larger samples would improve sampled softmax. The planned fix mixes uniform negatives with frequency-weighted negatives at p^0.75, following YouTube's approach to focus learning on popular items.
The plan now includes active dislikes as true hard negatives and a log-Q correction on negative logits to upweight rare items in the softmax. Hard-negative mining will sample four times the batch size and keep only the hardest negatives.
The log-Q correction for sampled softmax is being reconsidered. The standard correction would actually reduce the contribution of popular items as competitors, which may not improve retrieval quality. Empirical evidence suggests that skipping this correction or mixing uniform negatives often works better for ranking metrics. The approach is shifting away from the log-Q adjustment.
Using all thirty thousand catalog items as negatives was considered, but the full matrix multiplication would cost roughly 377 billion FLOPs per microbatch. That is too expensive for CPU training, so sampling remains necessary.
Full softmax over all thirty thousand items would cost roughly ten trillion FLOPs per epoch, so sampling is mandatory. A block of six thousand queries with two hundred fifty-six negatives consumes only three hundred fourteen million FLOPs, which is trivial on CPU. The real bottleneck is the GRU and user tower, not negative sampling. This means the negative budget can be increased substantially without hurting training speed.
A full cache of all thirty thousand item embeddings would be too expensive without FAISS, so that approach is abandoned. Periodic hard mining across the entire catalog is also too complex. The chosen compromise is to sample five hundred twelve popularity-mixed negatives per target and keep only the sixty-four hardest by current logits. This adds roughly six hundred twenty-nine million FLOPs per block, which is trivial on CPU.
The hard-negative mining strategy is being simplified. Rather than sampling five hundred twelve candidates and keeping only the hardest sixty-four, the plan now samples three hundred twenty negatives per target—uniform, popularity-weighted, and in-batch—and feeds the entire set directly into sampled softmax. Dislike items will be added as guaranteed hard negatives. A frequency distribution over all thirty thousand items is required for the popularity-weighted sampling.
The popularity distribution will reuse the same counts as build_item_correction. Sampling thirty thousand items with torch.multinomial is feasible on CPU for the batch size. Invalid negatives—known positives, likes, the target track itself, and duplicates—will be filtered out.
The existing resampling loop can still handle the larger negative budget. In-batch sampling will be expanded to draw up to sixty-four unique items from each user's history, rather than the current uniform random selection. The loss function is being rewritten from independent BCE into a single sampled softmax objective.
The sampled softmax loss will concatenate positive and negative logits, apply a temperature scaling, and use log-softmax over the combined vector. The positive loss is taken from index zero. A clamped temperature around 0.05–0.2 is being considered to keep logits in a stable range.
Pairwise BPR was considered as a simpler alternative, but sampled softmax is preferred for NDCG@10. Discovery-graded target weighting will be combined with the improved negative sampling to align training objectives with the evaluation metric.
The last-100 history filter may be the real problem. It risks using old positives as negatives, which would corrupt the negative pool. Full-history filtering is more correct for discovery. Target weighting will stay minimal since the task focuses on negatives.
A shared softmax across the entire microbatch is being considered. With six thousand queries, each example would have roughly six thousand in-batch negatives from one matrix multiplication. This would naturally replace the current uniform and popularity sampling with a much richer negative pool.
Shared items in the catalog create a false-negative risk in shared softmax. If two users both played item X, user A's query matching item X is correct, but the off-diagonal copy of X would be wrongly penalized as a negative. The fix masks logits where item_j equals the positive for row i, except the diagonal, and also masks any item already in that user's history.
The implementation will encode only unique positive items per microbatch, then compute logits with a [B, U] matrix multiplication where U is roughly 1500–4000. The loss targets are mapped back to the original batch indices via inverse. Random and popularity negatives are encoded separately and concatenated to this in-batch pool. This hybrid approach is standard and efficient.
The logits matrix will consume only about one hundred two megabytes, so memory is not a concern. The final negative pool will be built by mixing in-batch items, sixty-four popularity-sampled negatives, thirty-two uniform negatives, and up to eight active dislikes. This guarantees the model learns to rank tail and random items alongside popular ones.
The in-batch negative pool is being capped at forty-eight unique items per target. This avoids variable batch sizes and complex masking when the number of unique positives is small. The final negative budget is now fixed at one hundred forty-four: forty-eight uniform, forty-eight in-batch, and forty-eight popularity-sampled negatives.
The popularity distribution will use a power of 0.75 on training target counts. The log-Q correction for sampled negatives will be optional and default to off, since uniform negatives have no known sampling probability.
The mixed negative sampling strategy is being validated against prior research. In-batch negatives alone suppress popular items too aggressively, which explains why the current model’s eight in-batch negatives are insufficient. The temperature will be exposed as a tunable hyperparameter around 0.5, and raw dot products will be used instead of cosine similarity to avoid altering the serving path.
Training will run for two epochs with early stopping at one. The learning rate stays at 1e-3. Easy negatives may inflate pair accuracy, so a diagnostic check is deferred until full data preparation finishes. Data preparation will begin in the background while implementation proceeds. Files are being copied to /app/solution first.
Validation BCE will be replaced by the new sampled softmax loss for early stopping. With only one training epoch, early stopping cannot trigger, so two epochs with patience one are required instead. Data preparation is estimated at two to five minutes for forty-three million rows. The GRU training time is being estimated for eight CPUs.
The training window is only 128 recent targets plus 16 validation items, not the full sequence. The GRU processes this truncated window during training, while inference runs the complete history. This means roughly 1.2 million GRU steps are required per epoch with batch size 64.
The GRU forward pass is negligible, requiring only about 288 batched sequences per epoch. The real bottleneck is the loss computation: roughly 1.17 million targets, each with user tower and item encoding and thirty-two negatives, totaling approximately 550 billion FLOPs. On an eight-core CPU this could take ten to thirty seconds, though PyTorch CPU overhead may extend that.
The full block of roughly six thousand targets fits into a single microbatch of sixty-five thousand, so the optimizer will take about two hundred eighty-eight steps per epoch. Item encoding covers only about two thousand unique items per block. One training run is estimated at five to fifteen minutes, and two full validations would total twenty to forty minutes, leaving room for one validation experiment within the sixty-minute window. The plan is to implement the approach correctly and run the full validation rather than using a smaller subset.
Discovery-graded target weighting is being considered. It assigns higher gain to organic likes received within seven days of the listen, which better matches the NDCG objective than the current organic-strong scheme. The default organic-strong weighting will be kept unless discovery-graded proves superior.
Discovery-graded target weighting was rejected to keep the experiment clean. Non-completed listens were considered as weak negatives but dismissed as too noisy. The implementation is beginning.
Hard-k selection from an oversampled pool was considered but rejected. In-batch softmax already draws more negatives than the current model, so oversampling would not improve the expected hardest negative.
The final negative budget is fixed at 192: 64 uniform, 64 in-batch, 64 popularity-sampled negatives with a power of 0.75, and 8 active dislikes. The softmax temperature will be exposed as a tunable hyperparameter. Implementation requires updating the Config schema, the negative sampling logic, and the loss function to support this mixed pool.
Dislike negatives will be packed with padding_item = n_items and padded to eight per user with uniform items. They will not receive extra softmax weight. Validation BCE will be renamed to validation_loss to match the new loss. CLI flags for the new hyperparameters will be added. The data cache is unaffected by negative sampling changes. InfoNCE with cosine similarity was considered but rejected.
The current in-batch negatives are sampled uniformly from unique targets rather than the hardest. Computing logits for all unique in-batch items and selecting the top-k hardest would be more effective, though it requires encoding those items first.
The cleanest implementation is to encode the unique positives once and score them with in-batch softmax, then append the mixed extra negatives as additional columns. This avoids encoding the same items multiple times.
The implementation will encode unique in-batch positives once and score them against queries. Extra negatives are encoded separately and appended as additional columns. The true target class is the index of the positive within the unique set, with known false-negatives masked to negative infinity.
The current score_target_microbatches encodes positives and negatives independently per microbatch, so the loss computation must be restructured to share unique positive encodings across the batch. Known positives in history will be masked to negative infinity. Variable unique-positive counts across microbatches are acceptable, and training and validation will use the same path.
The in-batch softmax logic is confirmed correct: shared positive items across users remain as the true target class, while other users' next items are treated as negatives unless they appear in that user's history. The unique-positive count U is expected to be modest, around five hundred to two thousand, because many targets repeat within a batch.
The in-batch softmax risk of being too hard or popularity-biased is mitigated by mixing uniform, popularity-sampled, and dislike negatives. Extra columns are never the target class, so labels remain valid at index inv. The sampler already masks any extra negatives that are known positives. The core change is to draw these mixed negatives first, then encode shared in-batch positives once and score them against queries, appending the mixed negatives as additional columns. This restructures score_target_microbatches to avoid redundant encodings while preserving backward gradients through sequence vectors.
Popularity sampling will use item target counts raised to the power of 0.75, normalized to probabilities. The sampler draws these items via torch.multinomial with replacement.
The 8 in-batch negatives are dropped entirely. Every unique positive in the batch becomes a softmax class, so only uniform and popularity-sampled negatives remain. The old sampling code path is preserved for structure while the loss function is rewritten. A fixed softmax temperature of 1.0 is chosen over a learnable parameter.
Embedding growth could make the softmax peaky, but existing dropout and weight decay should mitigate this. Label smoothing is skipped. The implementation will copy the current model into /app/solution and modify train_current_model.py to apply the new negative sampling and loss. Invalid logits will be masked to -1e9 while preserving the true positive.
The implementation will keep two_tower_model.py mostly unchanged and copy it into the solution directory so the training script can import it. The loss function will be renamed from corrected_negative_loss to sampled_softmax_loss. Dislike negatives will be drawn from disliked_states.
Dislike negatives will be drawn from disliked_states and padded with uniform samples to fill the eight-item slot, since padding uses the item index n_items. All extra negatives will be pooled into the softmax. Default configuration values are being finalized.
The final configuration uses 48 uniform negatives, 48 popularity-sampled negatives, and 8 dislike negatives. In-batch negatives are removed entirely; all unique positives in the batch serve as softmax classes. The softmax temperature is fixed at 0.75. Even with a large block size, the maximum number of unique positives remains bounded by the batch dimension.
The microbatch size will remain high so the entire block fits in a single microbatch, ensuring every unique positive is included in the softmax. A simpler alternative is also being considered: keep per-row sampled negatives, increase their counts, add popularity sampling, and switch from BCE to sampled softmax. This approach is less invasive and carries less risk of bugs while still addressing the core retrieval issue.
The simpler, less invasive approach is selected. The existing negative sampler will be extended to draw uniform, popularity-weighted, and dislike negatives, while the loss switches to sampled softmax with temperature. Correction weights may be applied as logit offsets. The existing invalid filter remains unchanged.
Hard-negative mining will be added by oversampling the negative pool and keeping the hardest items plus remaining random samples. The total extra negatives are small enough to include all in the softmax. The conversion from multiplicative BCE correction weights into logit offsets for the sampled softmax is being verified.
The multiplicative correction from BCE is dropped entirely. Sampling probabilities differ across mixed negative sources, so the implementation will skip importance-sampling corrections for simplicity. A margin on the softmax is also rejected. The next code changes are adding new configuration fields and a build_item_sampling_probs() helper.
The concrete implementation plan is now set. The negative sampler will be rewritten for mixed sources, the loss function renamed to sampled_softmax_loss, and score_target_microbatches updated to concatenate positive logits with negatives and apply the softmax temperature. Item probabilities and disliked items will be passed through the scoring path. The remaining work covers CLI integration and a JSON architecture description.
The sampled softmax will use a logit bias derived from correction weights, subtracted from the concatenated logits before cross-entropy. Pair accuracy will remain unchanged. A hybrid BCE-plus-softmax loss was considered but rejected as overkill; the implementation will use pure sampled softmax.
Initial dot products between 192-dimensional embeddings are tiny, so temperature 1.0 is safe. If embedding norms grow during training, the softmax could become one-hot and gradients would vanish. To prevent this, logits will be normalized by the square root of the embedding dimension, following a scaled-dot-product style.
A learnable temperature parameter was considered but rejected to avoid altering the serving path. The final formula scales raw dot products by 1/sqrt(dim) and divides by a fixed softmax temperature of 1.0.
The 1/sqrt(dim) logit scaling was abandoned because it would flatten the initial softmax too much. A pairwise softplus loss was considered but rejected. The final choice is sampled softmax with temperature 2.0 to prevent peakiness, paired with many mixed negatives.
Focal loss on negatives was considered but rejected as unnecessary complexity. The implementation will proceed with sampled softmax, which typically outperforms BPR for top-k retrieval. Invalid items drawn from popularity sampling will be resampled uniformly. The CPU generator is well-suited for multinomial draws. Popularity probabilities can be built directly from the existing count estimation without constructing a separate correction tensor.
Hard negative mining was considered but deferred. The immediate priority is the full in-batch unique softmax combined with a larger pool of mixed negatives, which is the most impactful structural change.
The loss function is being refactored into a single retrieval_loss signature. It will accept query vectors, positive vectors, negative vectors, and an optional in-batch pool of unique positives with their labels and invalid-item masks. This replaces the previous separate logits-and-loss pattern.
The retrieval_loss implementation computes positive logits with a broadcast sum and negative logits via einsum. In-batch unique positives are scored against queries, then masked to negative infinity for invalid items and the true positive label.
The in-batch unique positives are appended to the logits and masked to negative infinity for invalid items and the true positive label, which is placed in column zero to avoid double-counting. The inbatch_invalid mask is built by checking packed known histories against the unique item pool.
The liked-items invalid mask is being vectorized to avoid loops. A full broadcast comparison across batch, liked items, and unique items would require roughly 614 million comparisons, which is acceptable. The packed-history byte and bit columns are also being prepared for the invalid-item filter.
The packed-history invalid mask is now implemented with a bitwise lookup. The true positive target will be masked only in the in-batch section, not in column zero, to avoid double-counting. The existing known-positive filter includes sequence items from offset through offset+target_count+1.
The known-positive filter covers the entire training window, including future items, not just causal history. This means the current target is already in the known-positive set, so the true positive must be masked only in the in-batch section and left untouched in column zero.
The full-window invalid mask correctly prevents future training items from being treated as negatives without leaking labels into features. Inference will remain unchanged. Training target frequencies are preferred over catalog popularity for the popularity-weighted negative sampler because they align more closely with the retrieval loss.
Popularity probabilities will use a 0.9/0.1 epsilon mix of item counts raised to the 0.75 power and uniform distribution to cover tail items. The author is starting to write code and prepare caches. They are weighing whether to run the full production baseline first, since that would consume too much time for both baseline and final submission.
The author decided to skip a full baseline re-run. Running both models would require three trainings, which is too slow. Instead, they will validate the improved model and submit it directly. The README will describe the production configuration as the baseline and report the measured improvement. If time allows, they may attempt a short baseline run.
A strict timeline is now set: copy the code, prepare caches, validate the improved model, evaluate, and submit. A full baseline run is impossible because the deadline is too tight; the production model will serve as the reference instead.
A single training epoch on the improved model may suffice for validation, since it overfits less than the full run. The submission stage will use train-plus-validation data with the same token budget and 128 targets per sequence.
Full-sequence inference is feasible despite longer histories, requiring roughly four thousand GRU chunks. The default target weighting of organic-strong is acceptable. A remaining sampling issue is that uniform negatives from the 30k catalog still include seen items and rare popular ones.
Popularity sampling fixes the tail coverage problem. The author realized that including all unique in-batch items would dominate the negative pool, making the loss almost pure in-batch softmax and over-suppressing popular items per the MNS paper. The plan is to limit the in-batch pool to roughly 64–128 items.
The author settled on a fixed 192-item negative pool: 64 uniform, 64 popularity-weighted, and 64 in-batch unique items. This replaces the previous all-in-batch approach and prevents the loss from becoming nearly pure in-batch softmax.
The in-batch negatives will be the hardest 64 items from the unique positive pool. After scoring queries against encoded unique positives, the true positive and false-negative masks are applied, and the top-k indices are gathered to form the hard negative vectors.
Hard in-batch mining must be deferred until after query vectors are computed, since it requires current model scores. The existing code draws negatives before scoring, so the hard negative selection will be inserted inside the loss function.
The score_target_microbatches function will be restructured to compute queries, encode unique positives, sample extra negatives, mine hard in-batch items, and then compute the loss. Hard selection via top-k indices is discrete, so gradients will flow through the selected negative scores rather than through the selection itself. The author is now ready to write the code using the virtual environment Python and the default eight CPU threads.
Dislikes from user state will be added as true negatives inside the loss. The serving contract and chronological data protocol remain unchanged. The author will set CPU thread limits to eight and begin copying files and writing focused changes.
The log-Q correction for popularity-sampled items is deferred to a later version. The final configuration sets 64 uniform negatives, 64 hardest in-batch negatives, 64 popularity negatives, and 8 dislike negatives. Popularity sampling will use a power of 0.75, and the softmax temperature will remain at 1.0.
The author added loss_type and inbatch_selection configuration flags, keeping BCE as a fallback for debugging. Pair accuracy will be monitored as the mean of positive-versus-negative comparisons. Item-to-probability lookup for popularity sampling will run on CPU. The resampling loop with 136 columns and 32 retries is expected to remain efficient. Duplicate checks across the expanded negative pool are being verified.
The duplicate check across 136 negative columns is O(K²) but remains efficient at batch size 6400. Unit tests are skipped to save time. The author is now starting implementation and cache preparation.
More negatives per step should improve signal in a single epoch. The learning rate will stay at 1e-3 for now, but the gradient clip may rise to 5.0 or the loss scaled by log(1+K) to handle the larger sampled-softmax gradients.
The gradient clip will stay at 2.0 for now. The author is writing the code and fixing candidate_vectors to reuse encoded unique positives for hard in-batch mining, then encoding all negatives into a single batch.
The author realized that hard in-batch negatives can reuse the same query-positive dot products already computed for the loss, avoiding redundant encoding. Invalid items will be masked to negative infinity before the top-k selection.
Invalid items will be masked to negative infinity before the top-k selection so they are excluded from the softmax. The author also needs to pass disliked items into the negative sampler, since currently only liked items are used for filtering.
Dislikes sampled as uniform negatives are valid true negatives, so they do not need to be filtered out. The author is drafting a take_dislike_negatives helper that draws random indices and backfills them with actual disliked items where available, skipping padding tokens.
Dislike negatives will be drawn by taking the first available slots from each user's disliked list and replacing any padding with uniform random indices. Items that equal the target will be filtered out of the negative pool. The author is now beginning implementation and will also return item sampling probabilities from the correction builder.
Popularity sampling will use (counts + 1)^0.75 probabilities normalized to sum to one for torch.multinomial. The author decided against log-uniform or rank-based alternatives. Only the trainer code will be modified; the core two-tower model will remain unchanged.