Posts tagged "llm"
5 posts found
Kolibri Post-Training from Scratch: Effort Labels, Format Rewards and a Gradient-Norm Trust Region
Building Aleph Alpha's Kolibri post-training recipe from the ground up: soft reasoning-effort labels drawn from log-length percentiles, attention-cost ordering for packed SFT batches, segment-aware group advantages, the p-norm format multiplier, the B-TV trust region, a closed-form per-token bound on the logit gradient, targeted on-policy self-distillation, the three-bucket curriculum, and the Merlin–Arthur game against hallucination. Every piece is derived on one German baker problem and a group of four rollouts.
Kolibri Pre-Training from Scratch: UniBPE, Hybrid Attention and Exact Quantile Balancing
Building Aleph Alpha's Kolibri base model from the ground up: the Unigram-loss merge score of UniBPE and why it is pointwise mutual information in disguise, the KV-cache arithmetic of 40 sliding-window and 10 NoPE blocks, sigmoid MoE routing with Exact Quantile Balancing and Load-Error Injection, the 1 − K/E signature of a router the bias has overridden, and the cumulative-learning-rate law behind hyperparameter transfer. Every step is derived on one German sentence and four tokens.
MiMo-V2.6: Scaling Reinforcement Learning from Scratch
Building Xiaomi's MiMo-V2.6 RL recipe from the ground up — the importance-sampled GRPO objective, group-relative advantages and dynamic sampling, hack zeroing, Groupwise Reward Synthesis, Groupwise Advantage Redistribution, the group-relative length penalty, mass-conserving segment penalties, top-p candidate replay, router freezing, and the Little's-law Sample Mixer — all derived step by step on one four-rollout coding group.
X-Token: Cross-Tokenizer Knowledge Distillation from Scratch
Building cross-tokenizer distillation from the ground up — why per-position KL breaks across tokenizers, DP span alignment, the chain-rule chunk merge, the projection matrix W, and two complementary losses (P-KL and H-KL) — with a full proof of GOLD's suppressive gradient and one running 2+3 example derived step by step.
Foundation Prior: How LLM Outputs Reshape Bayesian Beliefs
Deriving the Foundation Prior framework from scratch — why synthetic data is not real evidence, the constrained optimization that produces exponential tilting, the trust parameter λ, prompt heterogeneity through mixtures, calibration via real data, and the final posterior — all step by step with one coin example.