Can training on diverse related tasks be more efficient than task-specific training?
This explores whether training a model across several related tasks at once can beat training one task in isolation — and what the corpus says about why and when that's true.
This explores whether training across several related tasks can be more efficient than narrow, task-specific training — and the corpus suggests the answer is often yes, but with sharp conditions on *how* you combine the tasks. The cleanest example is function calling: breaking it into seven granular subtasks (nested calls, chaining, parallel functions, parameter detection, and so on) and training across all of them generalized better than dumping everything into one umbrella dataset, closing the gap with frontier models like GPT and Claude Can breaking function calling into subtasks improve model generalization?. The lesson isn't just "more data" — it's that explicit decomposition into related skills gives the model a richer signal than a monolithic task ever could.
But efficiency depends heavily on order and interference. Training related tasks jointly can actively hurt if their learning dynamics conflict: structured domains (math, code) shrink a model's output entropy while creative domains expand it, so blindly mixing them lets entropy collapse damage open-ended skills. Scheduling structured tasks first yielded a 6.2% gain over naive joint training Does training order reshape how models handle different task types?. So multi-task training is more efficient *when sequenced to exploit complementary dynamics* rather than thrown together.
The other recurring theme is that diverse training is a hedge against the collapse that narrow training causes. RL on a single objective tends to squeeze a model down to one reward-maximizing strategy — documented in both reasoning and search agents — and training on diverse demonstrations is what preserves the model's exploration breadth Does reinforcement learning squeeze exploration diversity in search agents?. Going further, explicitly rewarding semantic diversity during training didn't just maintain variety, it *catalyzed* higher quality across both creative and math tasks Can diversity optimization improve quality during language model training?. Diversity, in other words, can be a source of efficiency rather than a tax on it.
There's also a structural angle the reader might not expect: you can get multi-task benefits without paying the usual cost of forgetting. Splitting adaptation into slow weight updates and fast textual context reached equivalent performance 1.4–3× faster with far less catastrophic forgetting, reframing forgetting as a misallocation problem rather than an inherent cost of learning many things Can splitting adaptation into two channels reduce forgetting?. And rather than blending tasks into one set of weights at all, you can train composable expert vectors that mix dynamically at inference, letting a model specialize continually without the experts interfering with each other Can models dynamically activate expert skills at inference time?.
The surprising undercurrent across all of this: the win from diverse-task training is rarely about the model learning more *facts*. Instruction tuning experiments show much of what transfers is knowledge of the output space, not deep task understanding Does instruction tuning teach task understanding or output format?. So when diverse training works, it often works by teaching the model a broader, more robust map of how to behave — exactly the thing narrow task-specific training quietly throws away.
Sources 7 notes
Granite-20B-FunctionCalling shows that explicit training across seven granular subtasks—nested calls, chaining, parallel functions, name detection, parameter detection, next-best function, and response generation—generalizes better than umbrella datasets like ToolLLM. This multi-task approach closes the performance gap with GPT, Claude, and Gemini.
Omni-Thinker shows structured domains decrease output entropy while creative domains increase it. BWT-guided scheduling—training structured tasks first—yields 6.2% gains over joint training by preventing entropy collapse from damaging open-ended capabilities.
RL training compresses behavioral diversity in search agents through the same entropy collapse mechanism documented in reasoning—policies converge on narrow reward-maximizing strategies. SFT on diverse demonstrations preserves exploration breadth, suggesting diversity-preservation techniques are essential for RL search scaling.
DARLING jointly optimizes for quality and semantic diversity using a learned classifier, finding that diversity rewards catalyze exploration and produce higher-quality outputs than quality-only baselines across both creative and mathematical tasks.
Fast-Slow Training routes task-specific lessons into optimized prompts while keeping parameter updates minimal, reaching equivalent performance 1.4–3x faster with substantially less catastrophic forgetting and plasticity loss, demonstrating that forgetting is a misallocation problem rather than an inherent cost.
Transformer2 demonstrates that tuning only singular values within weight matrices produces composable expert vectors that dynamically mix at inference without interference, outperforming LoRA with fewer parameters and enabling continual specialization.
Models trained on semantically empty or deliberately incorrect instructions achieve comparable performance to those trained on full correct instructions, achieving 43% vs random baseline 42.6%. The semantic content of instructions appears largely irrelevant; what transfers is knowledge of the output space.