Roadmap — recent research (2024-2026 highlights)

The 2024-2026-era body of work that informed Tier 1-3. Each entry is just enough context to know whether to dig in.

Alignment / preference

• DPO — Rafailov et al., NeurIPS 2023. “Direct Preference Optimization.” The paper that displaced PPO/RLHF for most labs.
• KTO — Ethayarajh et al., 2024. “Model Alignment as Prospect Theoretic Optimization.” Single-label preference; no pairs needed.
• ORPO — Hong et al., 2024. “Reference-Free Monolithic Preference Optimization.” Merges SFT + DPO into one loss. Drops the reference model.
• SimPO — Meng et al., 2024. “Simple Preference Optimization with a Reference-Free Reward.” Reference-free DPO. Length-normalized objective.
• IPO — Azar et al., 2023. “A General Theoretical Paradigm to Understand Learning from Human Preferences.” Identity preference; stronger regularization than DPO.
• CPO — Xu et al., 2024. “Contrastive Preference Optimization.” Combines DPO with a behavior-cloning term.
• NEFTune — Jain et al., NeurIPS 2023. “Noisy Embeddings Improve Instruction Finetuning.” 3 lines of code; +5-10 points on AlpacaEval.

Parameter-efficient fine-tuning

See docs/lora_guide.md for the mechanics.

• DoRA — Liu et al., 2024. “DoRA: Weight-Decomposed Low-Rank Adaptation.” Magnitude + direction decomposition; consistently beats LoRA.
• GaLore — Zhao et al., 2024. “GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.” Full fine-tuning at LoRA memory.
• LoftQ — Li et al., ICLR 2024. “LoftQ: LoRA-Fine-Tuning-Aware Quantization.” Quantization-aware LoRA initialization.
• VeRA — Kopiczko et al., ICLR 2024. “VeRA: Vector-based Random Matrix Adaptation.” Shared random matrices; 10× smaller than LoRA.
• PISSA — Meng et al., 2024. “PiSSA: Principal Singular Values and Singular Vectors Adaptation.” SVD-based LoRA initialization for faster convergence.
• LoRA+ — Hayou et al., ICML 2024. “LoRA+: Efficient Low Rank Adaptation of Large Models.” Different LRs for A and B.
• rsLoRA — Kalajdzievski, 2023. “A Rank Stabilization Scaling Factor.”

Quantization

• GPTQ — Frantar et al., ICLR 2023. “GPTQ: Accurate Post-Training Quantization.”
• AWQ — Lin et al., MLSys 2024. “AWQ: Activation-aware Weight Quantization.”
• HQQ — Badri & Shaji, 2024. “Half-Quadratic Quantization.” Calibration-free int4.
• KIVI — Liu et al., 2024. “KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.”
• BitNet b1.58 — Ma et al., 2024. “The Era of 1-bit LLMs.” Ternary weights ({-1, 0, 1}) from scratch. Training-from-scratch; not post-training.

Inference / efficiency

• Speculative decoding — Leviathan et al., ICML 2023. “Fast Inference from Transformers via Speculative Decoding.”
• Medusa — Cai et al., 2024. “Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.”
• EAGLE / EAGLE-2 — Li et al., 2024. “EAGLE-2: Faster Inference of Language Models.”
• StreamingLLM — Xiao et al., ICLR 2024. “Efficient Streaming Language Models with Attention Sinks.”

Architecture variants

• Multi-Token Prediction (MTP) — Gloeckle et al., ICML 2024. “Better & Faster Large Language Models via Multi-token Prediction.” Used by DeepSeek-V3 and Meta. Predict K tokens per position. See docs/mtp.md.
• Differential Transformer — Microsoft 2024. “Differential Transformer.” Subtract a noise attention pattern from the signal one.
• Mixture of Depths — Raposo et al., 2024. “Mixture-of-Depths.” Route tokens through fewer layers.
• Mamba / Mamba-2 — Gu & Dao, 2023/2024. “Mamba: Linear-Time Sequence Modeling.”
• LASER — Sharma et al., ICLR 2024. “The Truth is in There: Improving Reasoning with Layer-Selective Rank Reduction.” Counterintuitively beneficial selective rank truncation.

Optimizers

• Sophia — Liu et al., 2023. “Sophia: A Scalable Stochastic Second-order Optimizer.”
• Lion — Chen et al., NeurIPS 2023. “Symbolic Discovery of Optimization Algorithms.”
• Muon — Jordan, 2024. Newton-Schulz orthogonalization on gradients. Notes by Keller Jordan. Adopted by the nanoGPT speedrun community for big wins.
• GaLore — see PEFT section above.
• LISA — Pan et al., 2024. “LISA: Layerwise Importance Sampling.”

Distillation

See docs/distillation.md for what’s shipped.

• Soft targets distillation — Hinton et al., 2015. The original.
• MiniLLM — Gu et al., ICLR 2024. “MiniLLM: Knowledge Distillation of Large Language Models.” KL-divergence variants for distilling LLMs.
• Distilling Step-by-Step — Hsieh et al., ACL 2023. “Distilling Step-by-Step!.” Distill reasoning chains.
• On-Policy Distillation Survey, April 2026 — confirms distillation is “the dominant technique for transferring frontier capabilities into smaller, deployable student models.” Validates our Tier 1.1.
• MiniPLM (Gu et al., NeurIPS 2024) — distillation for pre-training, not just post-training. Distill a small base FROM a big base. Novel.
• Knowledge Distillation with Training Wheels (Feb 2025) — student can “request help” from teacher at test time.

Synthetic data + curriculum

• Self-Instruct — Wang et al., 2023. “Self-Instruct: Aligning LMs with Self-Generated Instructions.”
• Evol-Instruct — Xu et al., 2024 (WizardLM). “WizardLM: Empowering LLMs to Follow Complex Instructions.”
• DoReMi — Xie et al., NeurIPS 2023. “DoReMi: Optimizing Data Mixtures.” Learns optimal data-domain ratios.
• TinyStories — Eldan & Li, 2023. “TinyStories: How Small Can Language Models Be?.” Established the sub-100M coherence threshold on a constrained vocabulary.
• Magpie — Xu et al., ICLR 2025. “Magpie: Alignment Data Synthesis from Scratch.” Generates high-quality SFT data by prompting aligned LLMs with just their pre-query templates. No seeds, no prompt engineering, no human labels. Models SFT’d on Magpie data surpass Llama-3-8B-Instruct.

2025-era — reasoning + RLVR + verifier-based training

The biggest single shift in 2025 was the move from “preference alignment is the post-training step” to “reasoning + RL on verifiable rewards is the post-training step.”

• DeepSeek-R1 / R1-Zero — DeepSeek-AI, Jan 2025. “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.” Showed that RL with verifiable rewards (math/code grading) on a strong base produces emergent chain-of-thought reasoning. R1-Zero is the variant that skipped SFT entirely and went straight from base to RL. Single most-cited result of 2025.
• GRPO (Group Relative Policy Optimization) — DeepSeek, 2024-2025. PPO variant used by R1 that doesn’t need a separate value network. Computes advantage from a group of rollouts’ relative rewards. Less memory than PPO, fits on smaller hardware.
• DAPO (Decoupled Clip and Dynamic sAmpling Policy Optimization) — ByteDance Seed & Tsinghua, March 2025. Beat DeepSeek-R1 by 3 points on AIME 2024 with 50% of R1’s training compute. Four techniques layered on top of GRPO: clip higher (asymmetric PPO clipping bounds), dynamic sampling (rejection sample bad rollouts before they enter the gradient), token-level policy gradients (per-token advantage rather than per-sequence), overlong reward shaping. Open source at BytedTsinghua-SIA/DAPO. Practical implication: GRPO is the right Tier 3 mental model; DAPO is the right Tier 3 implementation.
• RLVR (Reinforcement Learning from Verifiable Rewards) — umbrella for: math → grade with verifier; code → grade by running tests; reasoning → grade by deterministic checker. Allen AI’s Tulu-3 paper was the first open recipe; DeepSeek-R1 scaled it.
• OpenAI o1 / o3 — proprietary but reframed the field. The “test-time compute” thesis: hand the model more inference compute (chain-of-thought tokens) and it gets noticeably smarter.
• Reasoning distillation — DeepSeek-R1-Distill series: reasoning traces from R1 are used as SFT data for smaller models.
• OpenThoughts — community open-source dataset of reasoning traces. Built to be the open analog of R1’s reasoning data.
• Test-time compute scaling — separate axis from training scaling. Spend more inference tokens (CoT) for better outputs. Notable: Snell et al., 2024. “Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters.”
• DeepSeek-V3 — DeepSeek-AI, Dec 2024. “DeepSeek-V3 Technical Report.” 671B-MoE (37B active). Multi-Token Prediction during training.
• Qwen3 — Alibaba, 2025. Latest in the Qwen family (4B-397B).

What “2025-era post-training” looks like at our scale

The R1-class recipe at the 100M-1B scale:

1. Pretrain (still the same) — FineWeb-edu etc.
2. SFT on reasoning traces — use OpenThoughts or R1-distill data. Trains the model to emit <think>...</think> blocks before answers.
3. RL with verifiable rewards — GRPO/DAPO on math/code/format tasks with programmatic verifiers.

Distillation from R1-Distill-7B / 14B into a 100M model is plausible at our scale; full RLVR is probably a stretch.

Evolution Strategies — competitive again (genuinely surprising)

Evolution Strategies at Scale — Qiu et al., Sept 2025. First successful application of ES to billion-parameter LLM fine-tuning at full parameter scale (no dimensionality reduction). Findings:

• Beats PPO/DPO on long-horizon and delayed-reward tasks.
• More robust to reward hacking than RL.
• Better training stability — no value network to tune.
• Tolerates very high-dimensional parameter spaces.
• Open source: VsonicV/es-fine-tuning-paper.

Why this matters specifically for TinyGPT: ES is parallelizable across CPU workers (no GPU needed for the rollouts) and has lower per-step memory than PPO/GRPO. At our resource-constrained scale it could plausibly out-perform DPO/SimPO for instruction-following at the same wall-clock budget. Worth a real benchmark.

See docs/evolution_strategies.md.

FP4 training (parked — hardware-blocked)

Three papers in 2025 established that fully-quantized FP4 training (weights + activations + gradients all in FP4) reaches BF16-comparable quality:

• Wang et al., Jan 2025 — “Optimizing LLM Training Using FP4 Quantization.” Demonstrated at 13B params on 100B tokens.
• Microsoft & Nvidia FP4-All-the-Way, May 2025 — 200B-token validation.
• Quartet II / NVFP4, Jan 2026 — improved gradient estimator.

The format that wins is NVFP4: blocks of 16 FP4 values share a scale factor; stochastic rounding on backward+update, round-to-nearest on forward. Key empirical threshold: when gradient norm drops below ~√3 × quantization noise, FP4 training stops working — caps how deep into training you can stay in FP4.

For us: Mac M-series GPUs don’t have native FP4 ops yet (we’d simulate). bf16 → FP4 is a ~3-4× memory savings on top of bf16’s 2×. But the dependency on hardware FP4 means this is parked for TinyGPT until Apple silicon supports it. Listed in blockers.md.

2026 small-model landscape (relevant peers)

The competitive scale for “small model that’s actually useful” has shifted up since our project started:

• SmolLM3-3B — fully open instruct + reasoning model. Beats Llama-3.2-3B and Qwen2.5-3B on 12 benchmarks. Trained on ~600B tokens (SmolLM corpus = Cosmopedia + FineWeb-edu + Stack).
• Qwen3.5-0.8B — multimodal (text + vision) from scratch. Apache 2.0. Released Feb-March 2026.
• Phi-4-mini-instruct — Microsoft. Data-quality-over-scale thesis. Beats GPT-4o on MATH; beats Llama-3.2-3B across all benchmarks.
• Gemma-3n-E2B-IT — Google. On-device-focused. 2B with multi-modal.

Implication: the leaderboard’s “browser-trainable small model” niche is now distinctively educational + open-process, not performance-competitive with 2026 commercial small models. The leaderboard product narrative should emphasize “every byte of training code is here” + “trains in a tab” rather than “competes with Phi-4.”

Tools we should know about

• Unsloth — fine-tuning framework that fits 8B models on 12 GB consumer GPUs via custom Triton kernels + memory tricks. Not Mac/MLX-Swift, but worth studying for technique transfer.
• Argilla Distilabel — Python framework for synthetic SFT/DPO data generation pipelines. Wraps Magpie, DEITA, UltraFeedback recipes.
• DEITA (Liu et al., 2024) — instruction-tuning data quality framework. Scores complexity × quality × diversity for instruction sets.

Survey / overview reads

• State of GPT (Karpathy, 2023) — still the cleanest mental model of pretrain → SFT → RM → PPO. We’re skipping RM/PPO for DPO.
• A Survey of LLMs (Zhao et al., 2024) — “arXiv 2303.18223.” Continuously updated.
• HuggingFace Alignment Handbook — github.com/huggingface/alignment-handbook. The reference recipes for SFT/DPO at the 7B scale.
• AllenAI Tulu-3 paper — Lambert et al., 2024. “Tulu 3: Pushing Frontiers in Open Language Model Post-Training.”
• SmolLM blog post (Hugging Face, 2024) — blog. 135M / 360M / 1.7B fully-open small models with their training recipe.

Honest note on the cutoff

Assistant knowledge cutoff is January 2026. After that, items above were folded in via web search; spottier coverage of Feb-May 2026. If you have specific recent papers, datasets, or techniques in mind that aren’t listed, point me at the URL or name and they can be folded in.